https://recsyswiki.com/api.php?action=feedcontributions&feedformat=atom&user=Zeno+Gantner 2026-05-01T07:47:55Z User contributions MediaWiki 1.34.2 https://recsyswiki.com/index.php?title=Meta&diff=2697 2024-04-09T07:49:02Z

<p>Zeno Gantner: /* Software */</p> <hr /> <div>'''Meta''' is the company behind '''Facebook''', '''Instagram''', and '''WhatsApp'''.<br /> <br /> == Blog posts ==<br /> <br /> # [https://engineering.fb.com/2022/10/31/ml-applications/instagram-notification-management-machine-learning/ Improving Instagram notification management with machine learning and causal inference], 2022-10-31<br /> # [https://ai.facebook.com/blog/facebook-feed-improvements-ai-show-more-less/ The new AI-powered feature designed to improve Feed for everyone], 2022-10-05<br /> # [https://ai.facebook.com/tools/system-cards/instagram-feed-ranking/# What is the Instagram Feed?], 2022-02-23<br /> # [https://research.fb.com/blog/2021/08/when-do-recommender-systems-amplify-user-preferences-a-theoretical-framework-and-mitigation-strategies/ When do recommender systems amplify user preferences? A theoretical framework and mitigation strategies], 2021-08<br /> # [https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works Shedding More Light on How Instagram Works], 2021-06-08<br /> # [https://engineering.fb.com/2020/12/17/ml-applications/diversified-recommendations/ On the value of diversified recommendations], 2020-12-17<br /> # [https://engineering.fb.com/2020/12/10/web/how-instagram-suggests-new-content/ How Instagram suggests new content], 2020-12-10<br /> # [https://about.instagram.com/blog/engineering/designing-a-constrained-exploration-system Designing a Constrained Exploration System], 2020-12-10<br /> # [https://instagram-engineering.com/five-things-i-learned-about-working-on-content-quality-at-instagram-5031b1342bea Five things I learned about working on content quality at Instagram], 2020-01-25<br /> # {{production}} [https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882 Instagram’s Explore Recommender System], 2019-11-26 [https://news.ycombinator.com/item?id=21638216 HackerNews discussion]<br /> #* 3-part funnel (2 layers of [[candidate generation]])<br /> #* domain-specific language (We have separation between model and filter config).<br /> #* account embeddings<br /> #* embedding-based<br /> #* “See fewer posts like this” – [[explicit feedback]]<br /> # [https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15 Offline Policy Evaluation: Run fewer, better A/B tests]<br /> # [https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/ DLRM: An advanced, open source deep learning recommendation model], 2019-07-02<br /> # [https://instagram-engineering.com/lessons-learned-at-instagram-stories-and-feed-machine-learning-54f3aaa09e56 Lessons Learned at Instagram Stories and Feed Machine Learning], 2018-12-18<br /> # [https://research.facebook.com/blog/2018/9/efficient-tuning-of-online-systems-using-bayesian-optimization/ Efficient tuning of online systems using Bayesian optimization], 2018-09-17<br /> <br /> == Papers ==<br /> <br /> # {{ops}}{{performance}} [https://arxiv.org/pdf/2104.05158.pdf Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models], ISCA 2022 industry track<br /> # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]<br /> # {{ads}}{{neural}} [https://arxiv.org/pdf/2203.11014.pdf DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction], KDD 2022<br /> # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021<br /> # [https://research.fb.com/publications/preference-amplification-in-recommender-systems/ Preference Amplification in Recommender Systems], [[KDD 2021]]<br /> # {{search}} [https://www.kdd.org/kdd2020/accepted-papers/view/embedding-based-retrieval-in-facebook-search Embedding-based Retrieval in Facebook Search], [[KDD 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/compositional-embeddings-using-complementary-partitions-for-memory-efficien Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems], [[KDD 2020]]<br /> # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020<br /> # {{experimentation}} [https://dominiccoey.github.io/assets/papers/experiment_splitting.pdf Improving Treatment Effect Estimators Through Experiment Splitting], WWW 2019<br /> # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)<br /> # {{ads}} [https://research.fb.com/publications/counterfactual-reasoning-and-learning-systems-the-example-of-computational-advertising/ Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising]<br /> # [https://arxiv.org/abs/1909.11810 Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems]<br /> # {{ads}} [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014<br /> # [https://cs.stanford.edu/~jure/pubs/linkpred-wsdm11.pdf Supervised Random Walks: Predicting and Recommending Links in Social Networks], [[WSDM 2011]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/pytorch/torchrec TorchRec], [https://dl.acm.org/doi/10.1145/3523227.3547387 RecSys 2022 talk]<br /> # [https://github.com/facebookresearch/dlrm DLRM recommender]: click probability model<br /> # [https://github.com/facebook/prophet Prophet]: forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.<br /> # [https://github.com/facebook/FAI-PEP Facebook AI Performance Evaluation Platform]: framework and backend agnostic benchmarking platform to compare machine learning inferencing runtime metrics on a set of models and on variety of backends.<br /> # {{ltor}} [https://github.com/facebookresearch/StarSpace StarSpace]: Learning embeddings for classification, retrieval and ranking.<br /> # [https://github.com/facebookresearch/ReAgent ReAgent/Horizon]: end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. [https://reagent.ai/rasp_tutorial.html# Tutorial] contains e-commerce/recommendation example. [https://research.fb.com/publications/horizon-facebooks-open-source-applied-reinforcement-learning-platform/ paper]<br /> # [https://github.com/facebookresearch/faiss faiss]: library for efficient similarity search and clustering of dense vectors.<br /> # [https://github.com/facebookresearch/pysparnn pysparnn]: approximate nearest neighbor search for sparse data in Python.<br /> # [https://github.com/facebook/Ax Ax]: adaptive experimentation platform, [https://ax.dev/ ax.dev]<br /> <br /> == External links ==<br /> <br /> * [https://research.fb.com/blog/ research blog]<br /> * [https://research.fb.com/publications/ research publications]<br /> * [https://engineering.fb.com/ tech blog]<br /> * [https://instagram-engineering.com old Instagram tech blog]<br /> * GitHub repositories<br /> ** [https://github.com/facebook Facebook]<br /> ** [https://github.com/facebookresearch FB Research]<br /> ** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]<br /> ** [https://github.com/Instagram Instagram]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alphabet&diff=2696 2024-03-13T11:32:59Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alphabet''' is the parent company of '''Google''' and many of its (former) subsidiaries, for example '''YouTube''' and '''DeepMind'''.<br /> <br /> <br /> == Tutorials ==<br /> <br /> # [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning]<br /> # [https://developers.google.com/machine-learning/recommendation 4-hour tutorial on Recommendation Systems]<br /> <br /> == Papers ==<br /> <br /> # [https://dl.acm.org/doi/pdf/10.1145/3651170 Efficient Optimization of Sparse User Encoder Recommenders], ACM Transactions on Recommender Systems, 2024<br /> # [https://dl.acm.org/doi/pdf/10.1145/3604915.3608882 Efficient Data Representation Learning in Google-scale Systems], [[RecSys 2023]]<br /> # [https://dl.acm.org/doi/pdf/10.1145/3604915.3608792 Online Matching: A Real-time Bandit System for Large-scale Recommendations], RecSys 2023<br /> # [https://dl.acm.org/doi/pdf/10.1145/3600097 On Reducing User Interaction Data for Personalization], [[ACM TORS]], 2023<br /> # [https://arxiv.org/pdf/2306.01720.pdf Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation], [[KDD 2023]]<br /> # [https://arxiv.org/abs/2305.06474 Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction], ??? 2023<br /> # [https://arxiv.org/abs/2305.05065 Recommender Systems with Generative Retrieval], ??? 2023<br /> # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022<br /> # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]]<br /> # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022<br /> # {{ltor}}{{neural}}{{ops}} [https://arxiv.org/pdf/2209.05310.pdf On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models], [[RecSys 2022]]<br /> # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]]<br /> # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]]<br /> # {{ltor}}{{neural}} [https://openreview.net/pdf/033e77d13245fbc5492689e8b06afbfd433384d8.pdf Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?], [[ICLR 2021]]<br /> # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]]<br /> # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias<br /> # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems], [[WSDM 2021]]<br /> # {{bandits}} [https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html Latent Bandits Revisited], [[NeurIPS 2020]]<br /> # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]]<br /> # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2005.02553 Interpretable Learning-to-Rank with Generalized Additive Models], 2020<br /> # [https://dl.acm.org/doi/abs/10.1145/3366423.3380130 Off-policy Learning in Two-stage Recommender Systems], [[WWW 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3366424.3386195 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations], [[WWW 2020]]; Google Play<br /> # {{bias}} [https://research.google/pubs/pub49273/ Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies], [[KDD 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3383313.3412488 Neural Collaborative Filtering vs. Matrix Factorization Revisited], [[RecSys 2020]]<br /> # [https://arxiv.org/abs/2008.02930 Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval], [[CIKM 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2008.13535 DCN V2: Improved Deep &amp;amp; Cross Network and Practical Lessons for Web-scale Learning to Rank Systems], 2020<br /> # {{runtime}} [https://arxiv.org/abs/1908.10396 Accelerating Large-Scale Inference with Anisotropic Vector Quantization], 2019<br /> # [https://arxiv.org/pdf/1905.12767.pdf Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology], 2019<br /> # [https://arxiv.org/pdf/1810.02019.pdf Seq2Slate: Re-ranking and Slate Optimization with RNNs], 2019<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3331184.3331347 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks], [[SIGIR 2019]] (short paper)<br /> # {{neural}}[https://research.google/pubs/pub47954/ Towards Neural Mixture Recommender for Long Range Dependent User Sequences], [[WWW 2019]]<br /> # [https://arxiv.org/abs/1812.02353 Top-K Off-Policy Correction for a REINFORCE Recommender System], [[WSDM 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1902.08588 Towards neural mixture recommender for long range dependent user sequences], [[WWW 2019]]<br /> # {{ltor}} [https://daiwk.github.io/assets/youtube-multitask.pdf Recommending what video to watch next: A multitask ranking system], [[RecSys 2019]]<br /> # {{bias}} [https://research.google/pubs/pub48840/ Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations], [[RecSys 2019]]<br /> # [https://research.google/pubs/pub47705/ Efficient Training on Very Large Corpora via Gramian Estimation], [[ICLR 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3159652.3159727 Latent Cross: Making Use of Context in Recurrent Recommender Systems], [[WSDM 2018]]<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], [[KDD 2018]]<br /> # [https://research.google/pubs/pub47630/ Categorical-Attributes-Based Multi-Level Classification for Recommender Systems], [[RecSys 2018]]<br /> # {{neural}}{{bandits}} [https://openreview.net/pdf?id=SyYe6k-CW Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling], [[ICLR 2018]]<br /> # {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3269206.3271784 The LambdaLoss Framework for Ranking Metric Optimization], [[CIKM 2018]]<br /> # {{production}}{{experimentation}} ''[https://dl.acm.org/ft_gateway.cfm?id=3272018&amp;type=pdf Practical Diversified Recommendations on YouTube with Determinantal Point Processes]'', [[CIKM 2018]]<br /> # [https://datalab.csd.auth.gr/~gounaris/icde2018-google-recommendations.pdf Recommendations for All: Solving Thousands of Recommendation Problems Daily], ICDE 2018 (also describe user context representation by the actions taken)<br /> # [https://research.google/pubs/pub46300/ A Generic Coordinate Descent Framework for Learning from Implicit Feedback], [[WWW 2017]]<br /> # {{neural}} [https://arxiv.org/pdf/1708.05123.pdf Deep &amp;amp; Cross Network for Ad Click Predictions], 2017<br /> # {{production}} [https://dl.acm.org/ft_gateway.cfm?id=2959190&amp;type=pdf Deep neural networks for YouTube recommendations], [[RecSys 2016]]; [https://dl.acm.org/doi/abs/10.1145/2959100.2959190 video]<br /> # {{production}}{{neural}} [https://arxiv.org/abs/1606.07792 Wide &amp;amp; Deep Learning for Recommender Systems], DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], KDD 2018 (Google)<br /> # [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf Towards Conversational Recommender Systems], [[KDD 2016]]<br /> # {{ads}} [https://dl.acm.org/ft_gateway.cfm?id=2788583&amp;type=pdf Focusing on the Long-term: It’s Good for Users and Business], [[KDD 2015]]<br /> # {{ads}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41159.pdf Ad Click Prediction: a View from the Trenches], [[KDD 2013]]<br /> # {{production}} [https://tsinghua-nslab.github.io/seminar/2012Spring/11_3/YouTube_RecSys10.pdf The YouTube video recommendation system], [[RecSys 2010]]<br /> # [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.4329&amp;rep=rep1&amp;type=pdf Google news personalization: scalable online collaborative filtering], [[WWW 2007]]<br /> <br /> == Talks ==<br /> <br /> # [https://slideslive.com/38917655/reinforcement-learning-in-recommender-systems-some-challenges Reinforcement Learning for Recommender Systems: Some Challenges], ICML 2019<br /> <br /> == Blog posts ==<br /> <br /> # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07<br /> # [https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/ On YouTube's recommendation system], 2021-09-15<br /> # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21<br /> # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12<br /> # [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html Announcing ScaNN: Efficient Vector Similarity Search], 2020-07-28<br /> # [https://deepmind.com/blog/article/Advanced-machine-learning-helps-Play-Store-users-discover-personalised-apps Advanced machine learning helps Play Store users discover personalised apps], 2019-11-18<br /> <br /> == Software ==<br /> <br /> # [https://github.com/google/trax Trax]: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to [https://github.com/tensorflow/tensor2tensor tensor2tensor].<br /> # [https://github.com/google/rax rax], learning-to-rank framework for JAX, [https://research.google/pubs/pub51453/ paper]<br /> # [https://github.com/google-research/recsim RecSim] [https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html blog post]<br /> # [https://github.com/google-research/google-research/tree/master/scann ScaNN (Scalable Nearest Neighbors)]<br /> # [https://www.tensorflow.org/recommenders TensorFlow Recommenders] see also [https://github.com/tensorflow/community/blob/master/sigs/recommenders/CHARTER.md TF Recommenders SIG] and [https://github.com/tensorflow/recommenders-addons TF recommender add-ons]<br /> # [https://github.com/tensorflow/ranking TensorFlow Ranking]<br /> <br /> == External link ==<br /> <br /> * https://abc.xyz/<br /> * [https://en.wikipedia.org/wiki/Alphabet_Inc. Wikipedia article about Alphabet]<br /> * GitHub repositories:<br /> ** [https://github.com/google/ Google]<br /> ** [https://github.com/google-research/ Google Research] -- Google Research has a [https://github.com/google-research/google-research mono-repo] with most of their projects.<br /> ** [https://github.com/deepmind DeepMind]<br /> ** [https://github.com/youtube YouTube]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Xavier_Amatriain&diff=2695 2023-11-06T09:45:23Z

<p>Zeno Gantner: </p> <hr /> <div>{{Person<br /> |name = Xavier Amatriain<br /> |image = XavierAmatriain.jpg<br /> |affiliation = Netflix, Quora, Curai, '''LinkedIn'''<br /> |country = Los Gatos, California, USA<br /> |website = http://xavier.amatriain.net/<br /> &lt;!-- |user = --&gt; <br /> }}<br /> <br /> '''Xavier Amatriain''' worked as a researcher and manager at [[Netflix]].<br /> <br /> == Papers ==<br /> ...<br /> <br /> == Blog posts ==<br /> * [https://netflixtechblog.com/system-architectures-for-personalization-and-recommendation-e081aa94b5d8 System Architectures for Personalization and Recommendation], 2013-03-27<br /> * [https://amatriain.net/blog/RecsysArchitectures Blueprints for recommender system architectures: 10th anniversary edition], 2023<br /> <br /> <br /> [[Category: People]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Netflix&diff=2694 2023-11-06T09:45:17Z

<p>Zeno Gantner: </p> <hr /> <div>'''Netflix''' is a video streaming service. The [[Netflix Prize]], announced in 2006, helped to attract a lot of attention to the field of recommender systems.<br /> <br /> == Papers ==<br /> ...<br /> <br /> == Blog posts ==<br /> * [https://netflixtechblog.com/system-architectures-for-personalization-and-recommendation-e081aa94b5d8 System Architectures for Personalization and Recommendation], 2013-03-27<br /> <br /> <br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Zeno_Gantner&diff=2693 2023-11-06T09:43:06Z

<p>Zeno Gantner: </p> <hr /> <div>{{Person<br /> |name = Zeno Gantner<br /> |image = ZenoGantner.jpg<br /> |affiliation = [[Meta]]<br /> |country = Germany<br /> |website = http://ismll.de/personen/gantner_en.html<br /> |user = [[User:Zeno Gantner]]<br /> }}<br /> <br /> '''Zeno Gantner''' is the main author of the [[MyMediaLite]] recommender system library.<br /> <br /> [[Category:People]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alphabet&diff=2692 2023-10-04T17:01:48Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alphabet''' is the parent company of '''Google''' and many of its (former) subsidiaries, for example '''YouTube''' and '''DeepMind'''.<br /> <br /> <br /> == Tutorials ==<br /> <br /> # [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning]<br /> # [https://developers.google.com/machine-learning/recommendation 4-hour tutorial on Recommendation Systems]<br /> <br /> == Papers ==<br /> <br /> # [https://dl.acm.org/doi/pdf/10.1145/3604915.3608882 Efficient Data Representation Learning in Google-scale Systems], [[RecSys 2023]]<br /> # [https://dl.acm.org/doi/pdf/10.1145/3604915.3608792 Online Matching: A Real-time Bandit System for Large-scale Recommendations], RecSys 2023<br /> # [https://dl.acm.org/doi/pdf/10.1145/3600097 On Reducing User Interaction Data for Personalization], [[ACM TORS]], 2023<br /> # [https://arxiv.org/pdf/2306.01720.pdf Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation], [[KDD 2023]]<br /> # [https://arxiv.org/abs/2305.06474 Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction], ??? 2023<br /> # [https://arxiv.org/abs/2305.05065 Recommender Systems with Generative Retrieval], ??? 2023<br /> # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022<br /> # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]]<br /> # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022<br /> # {{ltor}}{{neural}}{{ops}} [https://arxiv.org/pdf/2209.05310.pdf On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models], [[RecSys 2022]]<br /> # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]]<br /> # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]]<br /> # {{ltor}}{{neural}} [https://openreview.net/pdf/033e77d13245fbc5492689e8b06afbfd433384d8.pdf Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?], [[ICLR 2021]]<br /> # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]]<br /> # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias<br /> # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems], [[WSDM 2021]]<br /> # {{bandits}} [https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html Latent Bandits Revisited], [[NeurIPS 2020]]<br /> # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]]<br /> # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2005.02553 Interpretable Learning-to-Rank with Generalized Additive Models], 2020<br /> # [https://dl.acm.org/doi/abs/10.1145/3366423.3380130 Off-policy Learning in Two-stage Recommender Systems], [[WWW 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3366424.3386195 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations], [[WWW 2020]]; Google Play<br /> # {{bias}} [https://research.google/pubs/pub49273/ Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies], [[KDD 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3383313.3412488 Neural Collaborative Filtering vs. Matrix Factorization Revisited], [[RecSys 2020]]<br /> # [https://arxiv.org/abs/2008.02930 Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval], [[CIKM 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2008.13535 DCN V2: Improved Deep &amp;amp; Cross Network and Practical Lessons for Web-scale Learning to Rank Systems], 2020<br /> # {{runtime}} [https://arxiv.org/abs/1908.10396 Accelerating Large-Scale Inference with Anisotropic Vector Quantization], 2019<br /> # [https://arxiv.org/pdf/1905.12767.pdf Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology], 2019<br /> # [https://arxiv.org/pdf/1810.02019.pdf Seq2Slate: Re-ranking and Slate Optimization with RNNs], 2019<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3331184.3331347 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks], [[SIGIR 2019]] (short paper)<br /> # {{neural}}[https://research.google/pubs/pub47954/ Towards Neural Mixture Recommender for Long Range Dependent User Sequences], [[WWW 2019]]<br /> # [https://arxiv.org/abs/1812.02353 Top-K Off-Policy Correction for a REINFORCE Recommender System], [[WSDM 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1902.08588 Towards neural mixture recommender for long range dependent user sequences], [[WWW 2019]]<br /> # {{ltor}} [https://daiwk.github.io/assets/youtube-multitask.pdf Recommending what video to watch next: A multitask ranking system], [[RecSys 2019]]<br /> # {{bias}} [https://research.google/pubs/pub48840/ Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations], [[RecSys 2019]]<br /> # [https://research.google/pubs/pub47705/ Efficient Training on Very Large Corpora via Gramian Estimation], [[ICLR 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3159652.3159727 Latent Cross: Making Use of Context in Recurrent Recommender Systems], [[WSDM 2018]]<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], [[KDD 2018]]<br /> # [https://research.google/pubs/pub47630/ Categorical-Attributes-Based Multi-Level Classification for Recommender Systems], [[RecSys 2018]]<br /> # {{neural}}{{bandits}} [https://openreview.net/pdf?id=SyYe6k-CW Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling], [[ICLR 2018]]<br /> # {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3269206.3271784 The LambdaLoss Framework for Ranking Metric Optimization], [[CIKM 2018]]<br /> # {{production}}{{experimentation}} ''[https://dl.acm.org/ft_gateway.cfm?id=3272018&amp;type=pdf Practical Diversified Recommendations on YouTube with Determinantal Point Processes]'', [[CIKM 2018]]<br /> # [https://datalab.csd.auth.gr/~gounaris/icde2018-google-recommendations.pdf Recommendations for All: Solving Thousands of Recommendation Problems Daily], ICDE 2018 (also describe user context representation by the actions taken)<br /> # [https://research.google/pubs/pub46300/ A Generic Coordinate Descent Framework for Learning from Implicit Feedback], [[WWW 2017]]<br /> # {{neural}} [https://arxiv.org/pdf/1708.05123.pdf Deep &amp;amp; Cross Network for Ad Click Predictions], 2017<br /> # {{production}} [https://dl.acm.org/ft_gateway.cfm?id=2959190&amp;type=pdf Deep neural networks for YouTube recommendations], [[RecSys 2016]]; [https://dl.acm.org/doi/abs/10.1145/2959100.2959190 video]<br /> # {{production}}{{neural}} [https://arxiv.org/abs/1606.07792 Wide &amp;amp; Deep Learning for Recommender Systems], DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], KDD 2018 (Google)<br /> # [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf Towards Conversational Recommender Systems], [[KDD 2016]]<br /> # {{ads}} [https://dl.acm.org/ft_gateway.cfm?id=2788583&amp;type=pdf Focusing on the Long-term: It’s Good for Users and Business], [[KDD 2015]]<br /> # {{ads}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41159.pdf Ad Click Prediction: a View from the Trenches], [[KDD 2013]]<br /> # {{production}} [https://tsinghua-nslab.github.io/seminar/2012Spring/11_3/YouTube_RecSys10.pdf The YouTube video recommendation system], [[RecSys 2010]]<br /> # [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.4329&amp;rep=rep1&amp;type=pdf Google news personalization: scalable online collaborative filtering], [[WWW 2007]]<br /> <br /> == Talks ==<br /> <br /> # [https://slideslive.com/38917655/reinforcement-learning-in-recommender-systems-some-challenges Reinforcement Learning for Recommender Systems: Some Challenges], ICML 2019<br /> <br /> == Blog posts ==<br /> <br /> # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07<br /> # [https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/ On YouTube's recommendation system], 2021-09-15<br /> # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21<br /> # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12<br /> # [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html Announcing ScaNN: Efficient Vector Similarity Search], 2020-07-28<br /> # [https://deepmind.com/blog/article/Advanced-machine-learning-helps-Play-Store-users-discover-personalised-apps Advanced machine learning helps Play Store users discover personalised apps], 2019-11-18<br /> <br /> == Software ==<br /> <br /> # [https://github.com/google/trax Trax]: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to [https://github.com/tensorflow/tensor2tensor tensor2tensor].<br /> # [https://github.com/google/rax rax], learning-to-rank framework for JAX, [https://research.google/pubs/pub51453/ paper]<br /> # [https://github.com/google-research/recsim RecSim] [https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html blog post]<br /> # [https://github.com/google-research/google-research/tree/master/scann ScaNN (Scalable Nearest Neighbors)]<br /> # [https://www.tensorflow.org/recommenders TensorFlow Recommenders] see also [https://github.com/tensorflow/community/blob/master/sigs/recommenders/CHARTER.md TF Recommenders SIG] and [https://github.com/tensorflow/recommenders-addons TF recommender add-ons]<br /> # [https://github.com/tensorflow/ranking TensorFlow Ranking]<br /> <br /> == External link ==<br /> <br /> * https://abc.xyz/<br /> * [https://en.wikipedia.org/wiki/Alphabet_Inc. Wikipedia article about Alphabet]<br /> * GitHub repositories:<br /> ** [https://github.com/google/ Google]<br /> ** [https://github.com/google-research/ Google Research] -- Google Research has a [https://github.com/google-research/google-research mono-repo] with most of their projects.<br /> ** [https://github.com/deepmind DeepMind]<br /> ** [https://github.com/youtube YouTube]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alphabet&diff=2691 2023-10-04T17:01:20Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alphabet''' is the parent company of '''Google''' and many of its (former) subsidiaries, for example '''YouTube''' and '''DeepMind'''.<br /> <br /> <br /> == Tutorials ==<br /> <br /> # [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning]<br /> # [https://developers.google.com/machine-learning/recommendation 4-hour tutorial on Recommendation Systems]<br /> <br /> == Papers ==<br /> <br /> # [https://dl.acm.org/doi/pdf/10.1145/3604915.3608882 Efficient Data Representation Learning in Google-scale Systems], [[RecSys 2023]]<br /> # [https://dl.acm.org/doi/pdf/10.1145/3604915.3608792 Online Matching: A Real-time Bandit System for Large-scale Recommendations], RecSys 2023<br /> # [https://dl.acm.org/doi/pdf/10.1145/3600097 On Reducing User Interaction Data for Personalization], [[ACM TORS]], 2023<br /> # [https://arxiv.org/pdf/2306.01720.pdf Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation], [[KDD 2023]]<br /> # [https://arxiv.org/abs/2305.06474 Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction], ??? 2023<br /> # [https://arxiv.org/abs/2305.05065 Recommender Systems with Generative Retrieval], ??? 2023<br /> # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022<br /> # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]]<br /> # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022<br /> # {{ltor}}{{neural}}{{ops}} [https://arxiv.org/pdf/2209.05310.pdf On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models], [[RecSys 2022]]<br /> # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]]<br /> # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]]<br /> # {{ltor}}{{neural}} [https://openreview.net/pdf/033e77d13245fbc5492689e8b06afbfd433384d8.pdf Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?], [[ICLR 2021]]<br /> # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]]<br /> # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias<br /> # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems]<br /> # {{bandits}} [https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html Latent Bandits Revisited], [[NeurIPS 2020]]<br /> # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]]<br /> # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2005.02553 Interpretable Learning-to-Rank with Generalized Additive Models], 2020<br /> # [https://dl.acm.org/doi/abs/10.1145/3366423.3380130 Off-policy Learning in Two-stage Recommender Systems], [[WWW 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3366424.3386195 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations], [[WWW 2020]]; Google Play<br /> # {{bias}} [https://research.google/pubs/pub49273/ Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies], [[KDD 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3383313.3412488 Neural Collaborative Filtering vs. Matrix Factorization Revisited], [[RecSys 2020]]<br /> # [https://arxiv.org/abs/2008.02930 Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval], [[CIKM 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2008.13535 DCN V2: Improved Deep &amp;amp; Cross Network and Practical Lessons for Web-scale Learning to Rank Systems], 2020<br /> # {{runtime}} [https://arxiv.org/abs/1908.10396 Accelerating Large-Scale Inference with Anisotropic Vector Quantization], 2019<br /> # [https://arxiv.org/pdf/1905.12767.pdf Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology], 2019<br /> # [https://arxiv.org/pdf/1810.02019.pdf Seq2Slate: Re-ranking and Slate Optimization with RNNs], 2019<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3331184.3331347 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks], [[SIGIR 2019]] (short paper)<br /> # {{neural}}[https://research.google/pubs/pub47954/ Towards Neural Mixture Recommender for Long Range Dependent User Sequences], [[WWW 2019]]<br /> # [https://arxiv.org/abs/1812.02353 Top-K Off-Policy Correction for a REINFORCE Recommender System], [[WSDM 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1902.08588 Towards neural mixture recommender for long range dependent user sequences], [[WWW 2019]]<br /> # {{ltor}} [https://daiwk.github.io/assets/youtube-multitask.pdf Recommending what video to watch next: A multitask ranking system], [[RecSys 2019]]<br /> # {{bias}} [https://research.google/pubs/pub48840/ Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations], [[RecSys 2019]]<br /> # [https://research.google/pubs/pub47705/ Efficient Training on Very Large Corpora via Gramian Estimation], [[ICLR 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3159652.3159727 Latent Cross: Making Use of Context in Recurrent Recommender Systems], [[WSDM 2018]]<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], [[KDD 2018]]<br /> # [https://research.google/pubs/pub47630/ Categorical-Attributes-Based Multi-Level Classification for Recommender Systems], [[RecSys 2018]]<br /> # {{neural}}{{bandits}} [https://openreview.net/pdf?id=SyYe6k-CW Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling], [[ICLR 2018]]<br /> # {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3269206.3271784 The LambdaLoss Framework for Ranking Metric Optimization], [[CIKM 2018]]<br /> # {{production}}{{experimentation}} ''[https://dl.acm.org/ft_gateway.cfm?id=3272018&amp;type=pdf Practical Diversified Recommendations on YouTube with Determinantal Point Processes]'', [[CIKM 2018]]<br /> # [https://datalab.csd.auth.gr/~gounaris/icde2018-google-recommendations.pdf Recommendations for All: Solving Thousands of Recommendation Problems Daily], ICDE 2018 (also describe user context representation by the actions taken)<br /> # [https://research.google/pubs/pub46300/ A Generic Coordinate Descent Framework for Learning from Implicit Feedback], [[WWW 2017]]<br /> # {{neural}} [https://arxiv.org/pdf/1708.05123.pdf Deep &amp;amp; Cross Network for Ad Click Predictions], 2017<br /> # {{production}} [https://dl.acm.org/ft_gateway.cfm?id=2959190&amp;type=pdf Deep neural networks for YouTube recommendations], [[RecSys 2016]]; [https://dl.acm.org/doi/abs/10.1145/2959100.2959190 video]<br /> # {{production}}{{neural}} [https://arxiv.org/abs/1606.07792 Wide &amp;amp; Deep Learning for Recommender Systems], DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], KDD 2018 (Google)<br /> # [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf Towards Conversational Recommender Systems], [[KDD 2016]]<br /> # {{ads}} [https://dl.acm.org/ft_gateway.cfm?id=2788583&amp;type=pdf Focusing on the Long-term: It’s Good for Users and Business], [[KDD 2015]]<br /> # {{ads}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41159.pdf Ad Click Prediction: a View from the Trenches], [[KDD 2013]]<br /> # {{production}} [https://tsinghua-nslab.github.io/seminar/2012Spring/11_3/YouTube_RecSys10.pdf The YouTube video recommendation system], [[RecSys 2010]]<br /> # [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.4329&amp;rep=rep1&amp;type=pdf Google news personalization: scalable online collaborative filtering], [[WWW 2007]]<br /> <br /> == Talks ==<br /> <br /> # [https://slideslive.com/38917655/reinforcement-learning-in-recommender-systems-some-challenges Reinforcement Learning for Recommender Systems: Some Challenges], ICML 2019<br /> <br /> == Blog posts ==<br /> <br /> # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07<br /> # [https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/ On YouTube's recommendation system], 2021-09-15<br /> # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21<br /> # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12<br /> # [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html Announcing ScaNN: Efficient Vector Similarity Search], 2020-07-28<br /> # [https://deepmind.com/blog/article/Advanced-machine-learning-helps-Play-Store-users-discover-personalised-apps Advanced machine learning helps Play Store users discover personalised apps], 2019-11-18<br /> <br /> == Software ==<br /> <br /> # [https://github.com/google/trax Trax]: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to [https://github.com/tensorflow/tensor2tensor tensor2tensor].<br /> # [https://github.com/google/rax rax], learning-to-rank framework for JAX, [https://research.google/pubs/pub51453/ paper]<br /> # [https://github.com/google-research/recsim RecSim] [https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html blog post]<br /> # [https://github.com/google-research/google-research/tree/master/scann ScaNN (Scalable Nearest Neighbors)]<br /> # [https://www.tensorflow.org/recommenders TensorFlow Recommenders] see also [https://github.com/tensorflow/community/blob/master/sigs/recommenders/CHARTER.md TF Recommenders SIG] and [https://github.com/tensorflow/recommenders-addons TF recommender add-ons]<br /> # [https://github.com/tensorflow/ranking TensorFlow Ranking]<br /> <br /> == External link ==<br /> <br /> * https://abc.xyz/<br /> * [https://en.wikipedia.org/wiki/Alphabet_Inc. Wikipedia article about Alphabet]<br /> * GitHub repositories:<br /> ** [https://github.com/google/ Google]<br /> ** [https://github.com/google-research/ Google Research] -- Google Research has a [https://github.com/google-research/google-research mono-repo] with most of their projects.<br /> ** [https://github.com/deepmind DeepMind]<br /> ** [https://github.com/youtube YouTube]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alibaba&diff=2690 2023-09-28T10:41:06Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alibaba''' is a Chinese e-commerce company.<br /> <br /> == Papers ==<br /> <br /> # {{search}} [https://arxiv.org/abs/1805.08524 Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search], [[IJCAI 2018]]<br /> # {{performance}} Wang et al.: [https://arxiv.org/pdf/1803.02349.pdf Billion-scale commodity embedding for e-commerce recommendation in Alibaba], [[KDD 2018]]<br /> # [https://arxiv.org/pdf/1801.02294.pdf Learning Tree-based Deep Model for Recommender Systems], KDD 2018<br /> # [https://arxiv.org/abs/1706.06978 Deep Interest Network for Click-Through Rate Prediction], KDD 2018<br /> # [https://arxiv.org/pdf/1905.09248.pdf Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction], KDD 2019<br /> # {{search}} {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3298689.3347000?download=true Personalized Re-ranking for Recommendation], [[RecSys 2019]]<br /> # {{neural}} [https://arxiv.org/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], [[AAAI 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1905.06874.pdf Behavior Sequence Transformer for E-commerce Recommendation in Alibaba], [[DLP-KDD workshop 2019]]<br /> # {{neural}} Zhu et al.: Joint Optimization of Tree-based Index and Deep Model for Recommender Systems, [[NIPS 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/abs/10.1145/3357384.3357895 BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer], [[CIKM 2019]]<br /> # [https://ieeexplore.ieee.org/abstract/document/9495161 Co-Displayed Items Aware List Recommendation], IEEE TODO, 2020<br /> # [https://dl.acm.org/doi/10.1145/3383313.3412238 PURS: Personalized Unexpected Recommender System for Improving User Satisfaction], [[RecSys 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/privileged-features-distillation-at-taobao-recommendations Privileged Features Distillation at Taobao Recommendations], [[KDD 2020]]<br /> # {{ltor}} [https://ieeexplore.ieee.org/abstract/document/9495161 AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online], IEEE TKDE 2021<br /> # {{ads}} [https://dl.acm.org/doi/abs/10.1145/3447548.3467086 Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling], [[KDD 2021]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/alibaba/EasyRec EasyRec]<br /> # [https://github.com/alibaba/DeepRec DeepRec]: uses TF 1.15<br /> # [https://github.com/shenweichen/DeepCTR-Torch DeepCTR] (by an Alibaba employee)<br /> # [https://github.com/shenweichen/DeepCTR-Torch DeepCTR-Torch] (by an Alibaba employee)<br /> <br /> == External links ==<br /> * [https://github.com/alibaba GitHub] (436 repos as of 2022-05, at least 2 of them relevant)<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2689 2023-09-28T10:39:40Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[Baidu]] -- [http://www.yichang-cs.com/jlu/WSDM21_unbiased.pdf] <br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * [[Booking.com]] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://www.researchgate.net/profile/Dmitri-Goldenberg/publication/349762238_Personalization_in_Practice_Methods_and_Applications/links/60409bf04585154e8c75323d/Personalization-in-Practice-Methods-and-Applications.pdf] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://dl.acm.org/doi/abs/10.1145/3460231.3474611] [https://dl.acm.org/doi/abs/10.1145/3511808.3557100] [https://dl.acm.org/doi/abs/10.1145/3292500.3330744] [http://www.toinebogers.com/workshops/complexrec2020/Mavridis.pdf] [https://arxiv.org/abs/2109.06723] [https://dl.acm.org/doi/abs/10.1145/3460231.3474611]<br /> * '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR], [https://dl.acm.org/doi/pdf/10.1145/3308558.3313447] [https://arxiv.org/pdf/2204.06240.pdf]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Pinterest]] -- [https://arxiv.org/pdf/1702.07969.pdf]<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[Yandex]] [https://openreview.net/pdf?id=MXfTQp8bZF]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2688 2023-09-11T12:48:27Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[Baidu]] -- [http://www.yichang-cs.com/jlu/WSDM21_unbiased.pdf] <br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * [[Booking.com]] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://www.researchgate.net/profile/Dmitri-Goldenberg/publication/349762238_Personalization_in_Practice_Methods_and_Applications/links/60409bf04585154e8c75323d/Personalization-in-Practice-Methods-and-Applications.pdf] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://dl.acm.org/doi/abs/10.1145/3460231.3474611] [https://dl.acm.org/doi/abs/10.1145/3511808.3557100] [https://dl.acm.org/doi/abs/10.1145/3292500.3330744] [http://www.toinebogers.com/workshops/complexrec2020/Mavridis.pdf] [https://arxiv.org/abs/2109.06723]<br /> * '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR], [https://dl.acm.org/doi/pdf/10.1145/3308558.3313447]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Pinterest]] -- [https://arxiv.org/pdf/1702.07969.pdf]<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[Yandex]] [https://openreview.net/pdf?id=MXfTQp8bZF]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2687 2023-09-11T12:42:52Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[Baidu]] -- [http://www.yichang-cs.com/jlu/WSDM21_unbiased.pdf] <br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * [[Booking.com]] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://www.researchgate.net/profile/Dmitri-Goldenberg/publication/349762238_Personalization_in_Practice_Methods_and_Applications/links/60409bf04585154e8c75323d/Personalization-in-Practice-Methods-and-Applications.pdf] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://dl.acm.org/doi/abs/10.1145/3460231.3474611] [https://dl.acm.org/doi/abs/10.1145/3511808.3557100] [https://dl.acm.org/doi/abs/10.1145/3292500.3330744] [http://www.toinebogers.com/workshops/complexrec2020/Mavridis.pdf]<br /> * '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR], [https://dl.acm.org/doi/pdf/10.1145/3308558.3313447]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Pinterest]] -- [https://arxiv.org/pdf/1702.07969.pdf]<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[Yandex]] [https://openreview.net/pdf?id=MXfTQp8bZF]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2686 2023-09-11T12:42:24Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[Baidu]] -- [http://www.yichang-cs.com/jlu/WSDM21_unbiased.pdf] <br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * [[Booking.com]] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://www.researchgate.net/profile/Dmitri-Goldenberg/publication/349762238_Personalization_in_Practice_Methods_and_Applications/links/60409bf04585154e8c75323d/Personalization-in-Practice-Methods-and-Applications.pdf] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://dl.acm.org/doi/abs/10.1145/3460231.3474611] [https://dl.acm.org/doi/abs/10.1145/3511808.3557100] [https://dl.acm.org/doi/abs/10.1145/3292500.3330744]<br /> * '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR], [https://dl.acm.org/doi/pdf/10.1145/3308558.3313447]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Pinterest]] -- [https://arxiv.org/pdf/1702.07969.pdf]<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[Yandex]] [https://openreview.net/pdf?id=MXfTQp8bZF]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alphabet&diff=2685 2023-08-08T08:54:20Z

<p>Zeno Gantner: /* Blog posts */</p> <hr /> <div>'''Alphabet''' is the parent company of '''Google''' and many of its (former) subsidiaries, for example '''YouTube''' and '''DeepMind'''.<br /> <br /> <br /> == Tutorials ==<br /> <br /> # [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning]<br /> # [https://developers.google.com/machine-learning/recommendation 4-hour tutorial on Recommendation Systems]<br /> <br /> == Papers ==<br /> <br /> # [https://dl.acm.org/doi/pdf/10.1145/3600097 On Reducing User Interaction Data for Personalization], [[ACM TORS]], 2023<br /> # [https://arxiv.org/pdf/2306.01720.pdf Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation], [[KDD 2023]]<br /> # [https://arxiv.org/abs/2305.06474 Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction], ??? 2023<br /> # [https://arxiv.org/abs/2305.05065 Recommender Systems with Generative Retrieval], ??? 2023<br /> # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022<br /> # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]]<br /> # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022<br /> # {{ltor}}{{neural}}{{ops}} [https://arxiv.org/pdf/2209.05310.pdf On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models], [[RecSys 2022]]<br /> # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]]<br /> # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]]<br /> # {{ltor}}{{neural}} [https://openreview.net/pdf/033e77d13245fbc5492689e8b06afbfd433384d8.pdf Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?], [[ICLR 2021]]<br /> # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]]<br /> # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias<br /> # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems]<br /> # {{bandits}} [https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html Latent Bandits Revisited], [[NeurIPS 2020]]<br /> # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]]<br /> # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2005.02553 Interpretable Learning-to-Rank with Generalized Additive Models], 2020<br /> # [https://dl.acm.org/doi/abs/10.1145/3366423.3380130 Off-policy Learning in Two-stage Recommender Systems], [[WWW 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3366424.3386195 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations], [[WWW 2020]]; Google Play<br /> # {{bias}} [https://research.google/pubs/pub49273/ Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies], [[KDD 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3383313.3412488 Neural Collaborative Filtering vs. Matrix Factorization Revisited], [[RecSys 2020]]<br /> # [https://arxiv.org/abs/2008.02930 Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval], [[CIKM 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2008.13535 DCN V2: Improved Deep &amp;amp; Cross Network and Practical Lessons for Web-scale Learning to Rank Systems], 2020<br /> # {{runtime}} [https://arxiv.org/abs/1908.10396 Accelerating Large-Scale Inference with Anisotropic Vector Quantization], 2019<br /> # [https://arxiv.org/pdf/1905.12767.pdf Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology], 2019<br /> # [https://arxiv.org/pdf/1810.02019.pdf Seq2Slate: Re-ranking and Slate Optimization with RNNs], 2019<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3331184.3331347 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks], [[SIGIR 2019]] (short paper)<br /> # {{neural}}[https://research.google/pubs/pub47954/ Towards Neural Mixture Recommender for Long Range Dependent User Sequences], [[WWW 2019]]<br /> # [https://arxiv.org/abs/1812.02353 Top-K Off-Policy Correction for a REINFORCE Recommender System], [[WSDM 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1902.08588 Towards neural mixture recommender for long range dependent user sequences], [[WWW 2019]]<br /> # {{ltor}} [https://daiwk.github.io/assets/youtube-multitask.pdf Recommending what video to watch next: A multitask ranking system], [[RecSys 2019]]<br /> # {{bias}} [https://research.google/pubs/pub48840/ Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations], [[RecSys 2019]]<br /> # [https://research.google/pubs/pub47705/ Efficient Training on Very Large Corpora via Gramian Estimation], [[ICLR 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3159652.3159727 Latent Cross: Making Use of Context in Recurrent Recommender Systems], [[WSDM 2018]]<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], [[KDD 2018]]<br /> # [https://research.google/pubs/pub47630/ Categorical-Attributes-Based Multi-Level Classification for Recommender Systems], [[RecSys 2018]]<br /> # {{neural}}{{bandits}} [https://openreview.net/pdf?id=SyYe6k-CW Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling], [[ICLR 2018]]<br /> # {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3269206.3271784 The LambdaLoss Framework for Ranking Metric Optimization], [[CIKM 2018]]<br /> # {{production}}{{experimentation}} ''[https://dl.acm.org/ft_gateway.cfm?id=3272018&amp;type=pdf Practical Diversified Recommendations on YouTube with Determinantal Point Processes]'', [[CIKM 2018]]<br /> # [https://datalab.csd.auth.gr/~gounaris/icde2018-google-recommendations.pdf Recommendations for All: Solving Thousands of Recommendation Problems Daily], ICDE 2018 (also describe user context representation by the actions taken)<br /> # [https://research.google/pubs/pub46300/ A Generic Coordinate Descent Framework for Learning from Implicit Feedback], [[WWW 2017]]<br /> # {{neural}} [https://arxiv.org/pdf/1708.05123.pdf Deep &amp;amp; Cross Network for Ad Click Predictions], 2017<br /> # {{production}} [https://dl.acm.org/ft_gateway.cfm?id=2959190&amp;type=pdf Deep neural networks for YouTube recommendations], [[RecSys 2016]]; [https://dl.acm.org/doi/abs/10.1145/2959100.2959190 video]<br /> # {{production}}{{neural}} [https://arxiv.org/abs/1606.07792 Wide &amp;amp; Deep Learning for Recommender Systems], DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], KDD 2018 (Google)<br /> # [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf Towards Conversational Recommender Systems], [[KDD 2016]]<br /> # {{ads}} [https://dl.acm.org/ft_gateway.cfm?id=2788583&amp;type=pdf Focusing on the Long-term: It’s Good for Users and Business], [[KDD 2015]]<br /> # {{ads}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41159.pdf Ad Click Prediction: a View from the Trenches], [[KDD 2013]]<br /> # {{production}} [https://tsinghua-nslab.github.io/seminar/2012Spring/11_3/YouTube_RecSys10.pdf The YouTube video recommendation system], [[RecSys 2010]]<br /> # [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.4329&amp;rep=rep1&amp;type=pdf Google news personalization: scalable online collaborative filtering], [[WWW 2007]]<br /> <br /> == Talks ==<br /> <br /> # [https://slideslive.com/38917655/reinforcement-learning-in-recommender-systems-some-challenges Reinforcement Learning for Recommender Systems: Some Challenges], ICML 2019<br /> <br /> == Blog posts ==<br /> <br /> # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07<br /> # [https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/ On YouTube's recommendation system], 2021-09-15<br /> # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21<br /> # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12<br /> # [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html Announcing ScaNN: Efficient Vector Similarity Search], 2020-07-28<br /> # [https://deepmind.com/blog/article/Advanced-machine-learning-helps-Play-Store-users-discover-personalised-apps Advanced machine learning helps Play Store users discover personalised apps], 2019-11-18<br /> <br /> == Software ==<br /> <br /> # [https://github.com/google/trax Trax]: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to [https://github.com/tensorflow/tensor2tensor tensor2tensor].<br /> # [https://github.com/google/rax rax], learning-to-rank framework for JAX, [https://research.google/pubs/pub51453/ paper]<br /> # [https://github.com/google-research/recsim RecSim] [https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html blog post]<br /> # [https://github.com/google-research/google-research/tree/master/scann ScaNN (Scalable Nearest Neighbors)]<br /> # [https://www.tensorflow.org/recommenders TensorFlow Recommenders] see also [https://github.com/tensorflow/community/blob/master/sigs/recommenders/CHARTER.md TF Recommenders SIG] and [https://github.com/tensorflow/recommenders-addons TF recommender add-ons]<br /> # [https://github.com/tensorflow/ranking TensorFlow Ranking]<br /> <br /> == External link ==<br /> <br /> * https://abc.xyz/<br /> * [https://en.wikipedia.org/wiki/Alphabet_Inc. Wikipedia article about Alphabet]<br /> * GitHub repositories:<br /> ** [https://github.com/google/ Google]<br /> ** [https://github.com/google-research/ Google Research] -- Google Research has a [https://github.com/google-research/google-research mono-repo] with most of their projects.<br /> ** [https://github.com/deepmind DeepMind]<br /> ** [https://github.com/youtube YouTube]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Meta&diff=2684 2023-08-07T09:53:48Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Meta''' is the company behind '''Facebook''', '''Instagram''', and '''WhatsApp'''.<br /> <br /> == Blog posts ==<br /> <br /> # [https://engineering.fb.com/2022/10/31/ml-applications/instagram-notification-management-machine-learning/ Improving Instagram notification management with machine learning and causal inference], 2022-10-31<br /> # [https://ai.facebook.com/blog/facebook-feed-improvements-ai-show-more-less/ The new AI-powered feature designed to improve Feed for everyone], 2022-10-05<br /> # [https://ai.facebook.com/tools/system-cards/instagram-feed-ranking/# What is the Instagram Feed?], 2022-02-23<br /> # [https://research.fb.com/blog/2021/08/when-do-recommender-systems-amplify-user-preferences-a-theoretical-framework-and-mitigation-strategies/ When do recommender systems amplify user preferences? A theoretical framework and mitigation strategies], 2021-08<br /> # [https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works Shedding More Light on How Instagram Works], 2021-06-08<br /> # [https://engineering.fb.com/2020/12/17/ml-applications/diversified-recommendations/ On the value of diversified recommendations], 2020-12-17<br /> # [https://engineering.fb.com/2020/12/10/web/how-instagram-suggests-new-content/ How Instagram suggests new content], 2020-12-10<br /> # [https://about.instagram.com/blog/engineering/designing-a-constrained-exploration-system Designing a Constrained Exploration System], 2020-12-10<br /> # [https://instagram-engineering.com/five-things-i-learned-about-working-on-content-quality-at-instagram-5031b1342bea Five things I learned about working on content quality at Instagram], 2020-01-25<br /> # {{production}} [https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882 Instagram’s Explore Recommender System], 2019-11-26 [https://news.ycombinator.com/item?id=21638216 HackerNews discussion]<br /> #* 3-part funnel (2 layers of [[candidate generation]])<br /> #* domain-specific language (We have separation between model and filter config).<br /> #* account embeddings<br /> #* embedding-based<br /> #* “See fewer posts like this” – [[explicit feedback]]<br /> # [https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15 Offline Policy Evaluation: Run fewer, better A/B tests]<br /> # [https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/ DLRM: An advanced, open source deep learning recommendation model], 2019-07-02<br /> # [https://instagram-engineering.com/lessons-learned-at-instagram-stories-and-feed-machine-learning-54f3aaa09e56 Lessons Learned at Instagram Stories and Feed Machine Learning], 2018-12-18<br /> # [https://research.facebook.com/blog/2018/9/efficient-tuning-of-online-systems-using-bayesian-optimization/ Efficient tuning of online systems using Bayesian optimization], 2018-09-17<br /> <br /> == Papers ==<br /> <br /> # {{ops}}{{performance}} [https://arxiv.org/pdf/2104.05158.pdf Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models], ISCA 2022 industry track<br /> # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]<br /> # {{ads}}{{neural}} [https://arxiv.org/pdf/2203.11014.pdf DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction], KDD 2022<br /> # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021<br /> # [https://research.fb.com/publications/preference-amplification-in-recommender-systems/ Preference Amplification in Recommender Systems], [[KDD 2021]]<br /> # {{search}} [https://www.kdd.org/kdd2020/accepted-papers/view/embedding-based-retrieval-in-facebook-search Embedding-based Retrieval in Facebook Search], [[KDD 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/compositional-embeddings-using-complementary-partitions-for-memory-efficien Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems], [[KDD 2020]]<br /> # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020<br /> # {{experimentation}} [https://dominiccoey.github.io/assets/papers/experiment_splitting.pdf Improving Treatment Effect Estimators Through Experiment Splitting], WWW 2019<br /> # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)<br /> # {{ads}} [https://research.fb.com/publications/counterfactual-reasoning-and-learning-systems-the-example-of-computational-advertising/ Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising]<br /> # [https://arxiv.org/abs/1909.11810 Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems]<br /> # {{ads}} [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014<br /> # [https://cs.stanford.edu/~jure/pubs/linkpred-wsdm11.pdf Supervised Random Walks: Predicting and Recommending Links in Social Networks], [[WSDM 2011]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/facebookresearch/dlrm DLRM recommender]: click probability model<br /> # [https://github.com/facebook/prophet Prophet]: forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.<br /> # [https://github.com/facebook/FAI-PEP Facebook AI Performance Evaluation Platform]: framework and backend agnostic benchmarking platform to compare machine learning inferencing runtime metrics on a set of models and on variety of backends.<br /> # {{ltor}} [https://github.com/facebookresearch/StarSpace StarSpace]: Learning embeddings for classification, retrieval and ranking.<br /> # [https://github.com/facebookresearch/ReAgent ReAgent/Horizon]: end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. [https://reagent.ai/rasp_tutorial.html# Tutorial] contains e-commerce/recommendation example. [https://research.fb.com/publications/horizon-facebooks-open-source-applied-reinforcement-learning-platform/ paper]<br /> # [https://github.com/facebookresearch/faiss faiss]: library for efficient similarity search and clustering of dense vectors.<br /> # [https://github.com/facebookresearch/pysparnn pysparnn]: approximate nearest neighbor search for sparse data in Python.<br /> # [https://github.com/facebook/Ax Ax]: adaptive experimentation platform, [https://ax.dev/ ax.dev]<br /> <br /> == External links ==<br /> <br /> * [https://research.fb.com/blog/ research blog]<br /> * [https://research.fb.com/publications/ research publications]<br /> * [https://engineering.fb.com/ tech blog]<br /> * [https://instagram-engineering.com old Instagram tech blog]<br /> * GitHub repositories<br /> ** [https://github.com/facebook Facebook]<br /> ** [https://github.com/facebookresearch FB Research]<br /> ** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]<br /> ** [https://github.com/Instagram Instagram]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Meta&diff=2683 2023-08-07T09:53:36Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Meta''' is the company behind '''Facebook''', '''Instagram''', and '''WhatsApp'''.<br /> <br /> == Blog posts ==<br /> <br /> # [https://engineering.fb.com/2022/10/31/ml-applications/instagram-notification-management-machine-learning/ Improving Instagram notification management with machine learning and causal inference], 2022-10-31<br /> # [https://ai.facebook.com/blog/facebook-feed-improvements-ai-show-more-less/ The new AI-powered feature designed to improve Feed for everyone], 2022-10-05<br /> # [https://ai.facebook.com/tools/system-cards/instagram-feed-ranking/# What is the Instagram Feed?], 2022-02-23<br /> # [https://research.fb.com/blog/2021/08/when-do-recommender-systems-amplify-user-preferences-a-theoretical-framework-and-mitigation-strategies/ When do recommender systems amplify user preferences? A theoretical framework and mitigation strategies], 2021-08<br /> # [https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works Shedding More Light on How Instagram Works], 2021-06-08<br /> # [https://engineering.fb.com/2020/12/17/ml-applications/diversified-recommendations/ On the value of diversified recommendations], 2020-12-17<br /> # [https://engineering.fb.com/2020/12/10/web/how-instagram-suggests-new-content/ How Instagram suggests new content], 2020-12-10<br /> # [https://about.instagram.com/blog/engineering/designing-a-constrained-exploration-system Designing a Constrained Exploration System], 2020-12-10<br /> # [https://instagram-engineering.com/five-things-i-learned-about-working-on-content-quality-at-instagram-5031b1342bea Five things I learned about working on content quality at Instagram], 2020-01-25<br /> # {{production}} [https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882 Instagram’s Explore Recommender System], 2019-11-26 [https://news.ycombinator.com/item?id=21638216 HackerNews discussion]<br /> #* 3-part funnel (2 layers of [[candidate generation]])<br /> #* domain-specific language (We have separation between model and filter config).<br /> #* account embeddings<br /> #* embedding-based<br /> #* “See fewer posts like this” – [[explicit feedback]]<br /> # [https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15 Offline Policy Evaluation: Run fewer, better A/B tests]<br /> # [https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/ DLRM: An advanced, open source deep learning recommendation model], 2019-07-02<br /> # [https://instagram-engineering.com/lessons-learned-at-instagram-stories-and-feed-machine-learning-54f3aaa09e56 Lessons Learned at Instagram Stories and Feed Machine Learning], 2018-12-18<br /> # [https://research.facebook.com/blog/2018/9/efficient-tuning-of-online-systems-using-bayesian-optimization/ Efficient tuning of online systems using Bayesian optimization], 2018-09-17<br /> <br /> == Papers ==<br /> <br /> # {{ops}}{{performance}} [https://arxiv.org/pdf/2104.05158.pdf Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models], ISCA 2022 industry track<br /> # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]<br /> # {{ads}}{{neural}} [https://arxiv.org/pdf/2203.11014.pdf DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction], KDD 2022<br /> # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021<br /> # [https://research.fb.com/publications/preference-amplification-in-recommender-systems/ Preference Amplification in Recommender Systems], [[KDD 2021]]<br /> # {{search}} [https://www.kdd.org/kdd2020/accepted-papers/view/embedding-based-retrieval-in-facebook-search Embedding-based Retrieval in Facebook Search], [[KDD 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/compositional-embeddings-using-complementary-partitions-for-memory-efficien Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems], [[KDD 2020]]<br /> # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020<br /> # {{experimentation} [https://dominiccoey.github.io/assets/papers/experiment_splitting.pdf Improving Treatment Effect Estimators Through Experiment Splitting], WWW 2019<br /> # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)<br /> # {{ads}} [https://research.fb.com/publications/counterfactual-reasoning-and-learning-systems-the-example-of-computational-advertising/ Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising]<br /> # [https://arxiv.org/abs/1909.11810 Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems]<br /> # {{ads}} [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014<br /> # [https://cs.stanford.edu/~jure/pubs/linkpred-wsdm11.pdf Supervised Random Walks: Predicting and Recommending Links in Social Networks], [[WSDM 2011]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/facebookresearch/dlrm DLRM recommender]: click probability model<br /> # [https://github.com/facebook/prophet Prophet]: forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.<br /> # [https://github.com/facebook/FAI-PEP Facebook AI Performance Evaluation Platform]: framework and backend agnostic benchmarking platform to compare machine learning inferencing runtime metrics on a set of models and on variety of backends.<br /> # {{ltor}} [https://github.com/facebookresearch/StarSpace StarSpace]: Learning embeddings for classification, retrieval and ranking.<br /> # [https://github.com/facebookresearch/ReAgent ReAgent/Horizon]: end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. [https://reagent.ai/rasp_tutorial.html# Tutorial] contains e-commerce/recommendation example. [https://research.fb.com/publications/horizon-facebooks-open-source-applied-reinforcement-learning-platform/ paper]<br /> # [https://github.com/facebookresearch/faiss faiss]: library for efficient similarity search and clustering of dense vectors.<br /> # [https://github.com/facebookresearch/pysparnn pysparnn]: approximate nearest neighbor search for sparse data in Python.<br /> # [https://github.com/facebook/Ax Ax]: adaptive experimentation platform, [https://ax.dev/ ax.dev]<br /> <br /> == External links ==<br /> <br /> * [https://research.fb.com/blog/ research blog]<br /> * [https://research.fb.com/publications/ research publications]<br /> * [https://engineering.fb.com/ tech blog]<br /> * [https://instagram-engineering.com old Instagram tech blog]<br /> * GitHub repositories<br /> ** [https://github.com/facebook Facebook]<br /> ** [https://github.com/facebookresearch FB Research]<br /> ** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]<br /> ** [https://github.com/Instagram Instagram]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2682 2023-07-20T13:30:53Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[Baidu]] -- [http://www.yichang-cs.com/jlu/WSDM21_unbiased.pdf] <br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR], [https://dl.acm.org/doi/pdf/10.1145/3308558.3313447]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Pinterest]] -- [https://arxiv.org/pdf/1702.07969.pdf]<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[Yandex]] [https://openreview.net/pdf?id=MXfTQp8bZF]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2681 2023-07-20T12:34:42Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[Baidu]] -- [http://www.yichang-cs.com/jlu/WSDM21_unbiased.pdf] <br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR], [https://dl.acm.org/doi/pdf/10.1145/3308558.3313447]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[Yandex]] [https://openreview.net/pdf?id=MXfTQp8bZF]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alphabet&diff=2680 2023-07-20T12:26:14Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alphabet''' is the parent company of '''Google''' and many of its (former) subsidiaries, for example '''YouTube''' and '''DeepMind'''.<br /> <br /> <br /> == Tutorials ==<br /> <br /> # [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning]<br /> # [https://developers.google.com/machine-learning/recommendation 4-hour tutorial on Recommendation Systems]<br /> <br /> == Papers ==<br /> <br /> # [https://dl.acm.org/doi/pdf/10.1145/3600097 On Reducing User Interaction Data for Personalization], [[ACM TORS]], 2023<br /> # [https://arxiv.org/pdf/2306.01720.pdf Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation], [[KDD 2023]]<br /> # [https://arxiv.org/abs/2305.06474 Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction], ??? 2023<br /> # [https://arxiv.org/abs/2305.05065 Recommender Systems with Generative Retrieval], ??? 2023<br /> # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022<br /> # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]]<br /> # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022<br /> # {{ltor}}{{neural}}{{ops}} [https://arxiv.org/pdf/2209.05310.pdf On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models], [[RecSys 2022]]<br /> # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]]<br /> # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]]<br /> # {{ltor}}{{neural}} [https://openreview.net/pdf/033e77d13245fbc5492689e8b06afbfd433384d8.pdf Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?], [[ICLR 2021]]<br /> # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]]<br /> # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias<br /> # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems]<br /> # {{bandits}} [https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html Latent Bandits Revisited], [[NeurIPS 2020]]<br /> # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]]<br /> # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2005.02553 Interpretable Learning-to-Rank with Generalized Additive Models], 2020<br /> # [https://dl.acm.org/doi/abs/10.1145/3366423.3380130 Off-policy Learning in Two-stage Recommender Systems], [[WWW 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3366424.3386195 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations], [[WWW 2020]]; Google Play<br /> # {{bias}} [https://research.google/pubs/pub49273/ Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies], [[KDD 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3383313.3412488 Neural Collaborative Filtering vs. Matrix Factorization Revisited], [[RecSys 2020]]<br /> # [https://arxiv.org/abs/2008.02930 Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval], [[CIKM 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2008.13535 DCN V2: Improved Deep &amp;amp; Cross Network and Practical Lessons for Web-scale Learning to Rank Systems], 2020<br /> # {{runtime}} [https://arxiv.org/abs/1908.10396 Accelerating Large-Scale Inference with Anisotropic Vector Quantization], 2019<br /> # [https://arxiv.org/pdf/1905.12767.pdf Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology], 2019<br /> # [https://arxiv.org/pdf/1810.02019.pdf Seq2Slate: Re-ranking and Slate Optimization with RNNs], 2019<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3331184.3331347 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks], [[SIGIR 2019]] (short paper)<br /> # {{neural}}[https://research.google/pubs/pub47954/ Towards Neural Mixture Recommender for Long Range Dependent User Sequences], [[WWW 2019]]<br /> # [https://arxiv.org/abs/1812.02353 Top-K Off-Policy Correction for a REINFORCE Recommender System], [[WSDM 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1902.08588 Towards neural mixture recommender for long range dependent user sequences], [[WWW 2019]]<br /> # {{ltor}} [https://daiwk.github.io/assets/youtube-multitask.pdf Recommending what video to watch next: A multitask ranking system], [[RecSys 2019]]<br /> # {{bias}} [https://research.google/pubs/pub48840/ Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations], [[RecSys 2019]]<br /> # [https://research.google/pubs/pub47705/ Efficient Training on Very Large Corpora via Gramian Estimation], [[ICLR 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3159652.3159727 Latent Cross: Making Use of Context in Recurrent Recommender Systems], [[WSDM 2018]]<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], [[KDD 2018]]<br /> # [https://research.google/pubs/pub47630/ Categorical-Attributes-Based Multi-Level Classification for Recommender Systems], [[RecSys 2018]]<br /> # {{neural}}{{bandits}} [https://openreview.net/pdf?id=SyYe6k-CW Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling], [[ICLR 2018]]<br /> # {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3269206.3271784 The LambdaLoss Framework for Ranking Metric Optimization], [[CIKM 2018]]<br /> # {{production}}{{experimentation}} ''[https://dl.acm.org/ft_gateway.cfm?id=3272018&amp;type=pdf Practical Diversified Recommendations on YouTube with Determinantal Point Processes]'', [[CIKM 2018]]<br /> # [https://datalab.csd.auth.gr/~gounaris/icde2018-google-recommendations.pdf Recommendations for All: Solving Thousands of Recommendation Problems Daily], ICDE 2018 (also describe user context representation by the actions taken)<br /> # [https://research.google/pubs/pub46300/ A Generic Coordinate Descent Framework for Learning from Implicit Feedback], [[WWW 2017]]<br /> # {{neural}} [https://arxiv.org/pdf/1708.05123.pdf Deep &amp;amp; Cross Network for Ad Click Predictions], 2017<br /> # {{production}} [https://dl.acm.org/ft_gateway.cfm?id=2959190&amp;type=pdf Deep neural networks for YouTube recommendations], [[RecSys 2016]]; [https://dl.acm.org/doi/abs/10.1145/2959100.2959190 video]<br /> # {{production}}{{neural}} [https://arxiv.org/abs/1606.07792 Wide &amp;amp; Deep Learning for Recommender Systems], DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], KDD 2018 (Google)<br /> # [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf Towards Conversational Recommender Systems], [[KDD 2016]]<br /> # {{ads}} [https://dl.acm.org/ft_gateway.cfm?id=2788583&amp;type=pdf Focusing on the Long-term: It’s Good for Users and Business], [[KDD 2015]]<br /> # {{ads}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41159.pdf Ad Click Prediction: a View from the Trenches], [[KDD 2013]]<br /> # {{production}} [https://tsinghua-nslab.github.io/seminar/2012Spring/11_3/YouTube_RecSys10.pdf The YouTube video recommendation system], [[RecSys 2010]]<br /> # [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.4329&amp;rep=rep1&amp;type=pdf Google news personalization: scalable online collaborative filtering], [[WWW 2007]]<br /> <br /> == Talks ==<br /> <br /> # [https://slideslive.com/38917655/reinforcement-learning-in-recommender-systems-some-challenges Reinforcement Learning for Recommender Systems: Some Challenges], ICML 2019<br /> <br /> == Blog posts ==<br /> <br /> # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07<br /> # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21<br /> # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12<br /> # [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html Announcing ScaNN: Efficient Vector Similarity Search], 2020-07-28<br /> # [https://deepmind.com/blog/article/Advanced-machine-learning-helps-Play-Store-users-discover-personalised-apps Advanced machine learning helps Play Store users discover personalised apps], 2019-11-18<br /> <br /> == Software ==<br /> <br /> # [https://github.com/google/trax Trax]: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to [https://github.com/tensorflow/tensor2tensor tensor2tensor].<br /> # [https://github.com/google/rax rax], learning-to-rank framework for JAX, [https://research.google/pubs/pub51453/ paper]<br /> # [https://github.com/google-research/recsim RecSim] [https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html blog post]<br /> # [https://github.com/google-research/google-research/tree/master/scann ScaNN (Scalable Nearest Neighbors)]<br /> # [https://www.tensorflow.org/recommenders TensorFlow Recommenders] see also [https://github.com/tensorflow/community/blob/master/sigs/recommenders/CHARTER.md TF Recommenders SIG] and [https://github.com/tensorflow/recommenders-addons TF recommender add-ons]<br /> # [https://github.com/tensorflow/ranking TensorFlow Ranking]<br /> <br /> == External link ==<br /> <br /> * https://abc.xyz/<br /> * [https://en.wikipedia.org/wiki/Alphabet_Inc. Wikipedia article about Alphabet]<br /> * GitHub repositories:<br /> ** [https://github.com/google/ Google]<br /> ** [https://github.com/google-research/ Google Research] -- Google Research has a [https://github.com/google-research/google-research mono-repo] with most of their projects.<br /> ** [https://github.com/deepmind DeepMind]<br /> ** [https://github.com/youtube YouTube]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alphabet&diff=2679 2023-07-20T12:24:58Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alphabet''' is the parent company of '''Google''' and many of its (former) subsidiaries, for example '''YouTube''' and '''DeepMind'''.<br /> <br /> <br /> == Tutorials ==<br /> <br /> # [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning]<br /> # [https://developers.google.com/machine-learning/recommendation 4-hour tutorial on Recommendation Systems]<br /> <br /> == Papers ==<br /> <br /> # [https://dl.acm.org/doi/pdf/10.1145/3600097 On Reducing User Interaction Data for Personalization], [[ACM TORS]], 2023<br /> # [https://arxiv.org/pdf/2306.01720.pdf Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation], [[KDD 2023]]<br /> # [https://arxiv.org/abs/2305.06474 Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction], ??? 2023<br /> # [https://arxiv.org/abs/2305.05065 Recommender Systems with Generative Retrieval], ??? 2023<br /> # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022<br /> # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]]<br /> # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022<br /> # {{ltor}}{{neural}}{{ops}} [https://arxiv.org/pdf/2209.05310.pdf On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models], [[RecSys 2022]]<br /> # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]]<br /> # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]]<br /> # {{ltor}}{{neural}} [https://openreview.net/pdf/033e77d13245fbc5492689e8b06afbfd433384d8.pdf Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?], [[ICLR 2021]]<br /> # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]]<br /> # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias<br /> # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems]<br /> # {{bandits}} [[https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html|Latent Bandits Revisited]], [[NeurIPS 2020]]<br /> # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]]<br /> # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2005.02553 Interpretable Learning-to-Rank with Generalized Additive Models], 2020<br /> # [https://dl.acm.org/doi/abs/10.1145/3366423.3380130 Off-policy Learning in Two-stage Recommender Systems], [[WWW 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3366424.3386195 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations], [[WWW 2020]]; Google Play<br /> # {{bias}} [https://research.google/pubs/pub49273/ Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies], [[KDD 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3383313.3412488 Neural Collaborative Filtering vs. Matrix Factorization Revisited], [[RecSys 2020]]<br /> # [https://arxiv.org/abs/2008.02930 Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval], [[CIKM 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2008.13535 DCN V2: Improved Deep &amp;amp; Cross Network and Practical Lessons for Web-scale Learning to Rank Systems], 2020<br /> # {{runtime}} [https://arxiv.org/abs/1908.10396 Accelerating Large-Scale Inference with Anisotropic Vector Quantization], 2019<br /> # [https://arxiv.org/pdf/1905.12767.pdf Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology], 2019<br /> # [https://arxiv.org/pdf/1810.02019.pdf Seq2Slate: Re-ranking and Slate Optimization with RNNs], 2019<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3331184.3331347 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks], [[SIGIR 2019]] (short paper)<br /> # {{neural}}[https://research.google/pubs/pub47954/ Towards Neural Mixture Recommender for Long Range Dependent User Sequences], [[WWW 2019]]<br /> # [https://arxiv.org/abs/1812.02353 Top-K Off-Policy Correction for a REINFORCE Recommender System], [[WSDM 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1902.08588 Towards neural mixture recommender for long range dependent user sequences], [[WWW 2019]]<br /> # {{ltor}} [https://daiwk.github.io/assets/youtube-multitask.pdf Recommending what video to watch next: A multitask ranking system], [[RecSys 2019]]<br /> # {{bias}} [https://research.google/pubs/pub48840/ Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations], [[RecSys 2019]]<br /> # [https://research.google/pubs/pub47705/ Efficient Training on Very Large Corpora via Gramian Estimation], [[ICLR 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3159652.3159727 Latent Cross: Making Use of Context in Recurrent Recommender Systems], [[WSDM 2018]]<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], [[KDD 2018]]<br /> # [https://research.google/pubs/pub47630/ Categorical-Attributes-Based Multi-Level Classification for Recommender Systems], [[RecSys 2018]]<br /> # {{neural}}{{bandits}} [https://openreview.net/pdf?id=SyYe6k-CW Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling], [[ICLR 2018]]<br /> # {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3269206.3271784 The LambdaLoss Framework for Ranking Metric Optimization], [[CIKM 2018]]<br /> # {{production}}{{experimentation}} ''[https://dl.acm.org/ft_gateway.cfm?id=3272018&amp;type=pdf Practical Diversified Recommendations on YouTube with Determinantal Point Processes]'', [[CIKM 2018]]<br /> # [https://datalab.csd.auth.gr/~gounaris/icde2018-google-recommendations.pdf Recommendations for All: Solving Thousands of Recommendation Problems Daily], ICDE 2018 (also describe user context representation by the actions taken)<br /> # [https://research.google/pubs/pub46300/ A Generic Coordinate Descent Framework for Learning from Implicit Feedback], [[WWW 2017]]<br /> # {{neural}} [https://arxiv.org/pdf/1708.05123.pdf Deep &amp;amp; Cross Network for Ad Click Predictions], 2017<br /> # {{production}} [https://dl.acm.org/ft_gateway.cfm?id=2959190&amp;type=pdf Deep neural networks for YouTube recommendations], [[RecSys 2016]]; [https://dl.acm.org/doi/abs/10.1145/2959100.2959190 video]<br /> # {{production}}{{neural}} [https://arxiv.org/abs/1606.07792 Wide &amp;amp; Deep Learning for Recommender Systems], DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], KDD 2018 (Google)<br /> # [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf Towards Conversational Recommender Systems], [[KDD 2016]]<br /> # {{ads}} [https://dl.acm.org/ft_gateway.cfm?id=2788583&amp;type=pdf Focusing on the Long-term: It’s Good for Users and Business], [[KDD 2015]]<br /> # {{ads}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41159.pdf Ad Click Prediction: a View from the Trenches], [[KDD 2013]]<br /> # {{production}} [https://tsinghua-nslab.github.io/seminar/2012Spring/11_3/YouTube_RecSys10.pdf The YouTube video recommendation system], [[RecSys 2010]]<br /> # [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.4329&amp;rep=rep1&amp;type=pdf Google news personalization: scalable online collaborative filtering], [[WWW 2007]]<br /> <br /> == Talks ==<br /> <br /> # [https://slideslive.com/38917655/reinforcement-learning-in-recommender-systems-some-challenges Reinforcement Learning for Recommender Systems: Some Challenges], ICML 2019<br /> <br /> == Blog posts ==<br /> <br /> # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07<br /> # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21<br /> # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12<br /> # [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html Announcing ScaNN: Efficient Vector Similarity Search], 2020-07-28<br /> # [https://deepmind.com/blog/article/Advanced-machine-learning-helps-Play-Store-users-discover-personalised-apps Advanced machine learning helps Play Store users discover personalised apps], 2019-11-18<br /> <br /> == Software ==<br /> <br /> # [https://github.com/google/trax Trax]: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to [https://github.com/tensorflow/tensor2tensor tensor2tensor].<br /> # [https://github.com/google/rax rax], learning-to-rank framework for JAX, [https://research.google/pubs/pub51453/ paper]<br /> # [https://github.com/google-research/recsim RecSim] [https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html blog post]<br /> # [https://github.com/google-research/google-research/tree/master/scann ScaNN (Scalable Nearest Neighbors)]<br /> # [https://www.tensorflow.org/recommenders TensorFlow Recommenders] see also [https://github.com/tensorflow/community/blob/master/sigs/recommenders/CHARTER.md TF Recommenders SIG] and [https://github.com/tensorflow/recommenders-addons TF recommender add-ons]<br /> # [https://github.com/tensorflow/ranking TensorFlow Ranking]<br /> <br /> == External link ==<br /> <br /> * https://abc.xyz/<br /> * [https://en.wikipedia.org/wiki/Alphabet_Inc. Wikipedia article about Alphabet]<br /> * GitHub repositories:<br /> ** [https://github.com/google/ Google]<br /> ** [https://github.com/google-research/ Google Research] -- Google Research has a [https://github.com/google-research/google-research mono-repo] with most of their projects.<br /> ** [https://github.com/deepmind DeepMind]<br /> ** [https://github.com/youtube YouTube]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Amazon&diff=2678 2023-07-17T09:54:03Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Amazon''' is the largest online retailer as well, with its subsidiary Amazon Web Services (AWS), the largest cloud provider in the Western world.<br /> They were also one of the first, if not the first, commercial user of recommendation systems.<br /> AWS also offers [[recommendations as a service]] with their product [[AWS Personalize]].<br /> <br /> <br /> == Papers ==<br /> <br /> # [https://www.amazon.science/publications?f0=0000016e-2ff2-d205-a5ef-affb543e0000&amp;s=0 all search and information retrieval publications by Amazon]<br /> # [http://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Amazon-Recommendations.pdf Amazon.com Recommendations: Item-to-Item Collaborative Filtering], IEEE Internet Computing, 2003<br /> # [https://dl.acm.org/doi/pdf/10.1145/2764468.2764488 Estimating the Causal Impact of Recommendation Systems from Observational Data], EC 2015<br /> # [https://dl.acm.org/doi/10.1145/2783258.2788579 One-Pass Ranking Models for Low-Latency Product Recommendations], [[KDD 2015]]<br /> # {{visual}} [https://dl.acm.org/doi/pdf/10.1145/2959100.2959171 Adaptive, personalized diversity for visual discovery], [[RecSys 2016]] (best short paper)<br /> # [https://www.amazon.science/publications/diversifying-music-recommendations Diversifying Music Recommendations], [[ICML 2016]]<br /> # [https://www.amazon.science/publications/sustainability-at-scale-towards-bridging-the-intention-behavior-gap-with-sustainable-recommendations Sustainability at Scale: Towards Bridging the Intention-Behavior Gap with Sustainable Recommendations], [[RecSys 2017]]<br /> # [https://www.amazon.science/publications/recommending-product-sizes-to-customers Recommending Product Sizes to Customers], RecSys 2017<br /> # {{production}} [https://www.amazon.science/publications/two-decades-of-recommender-systems-at-amazon-com Two Decades of Recommender Systems at Amazon.com], 2017<br /> # [https://www.amazon.science/publications/intent-based-relevance-estimation-from-click-logs Intent Based Relevance Estimation from Click Logs], [[CIKM 2017]]<br /> # [https://www.amazon.science/publications/mrnet-product2vec-a-multi-task-recurrent-neural-network-for-product-embeddings MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings], ECML-PKDD 2017<br /> # {{ltor}}{{bandits}} [https://arxiv.org/abs/2004.13106 Learning to Rank in the Position Based Model with Bandit Feedback] (Amazon Music)<br /> # {{bandits}} [https://arxiv.org/abs/2004.13576 A Linear Bandit for Seasonal Environments] (Amazon Music)<br /> # [https://www.amazon.science/publications/an-efficient-neighborhood-based-interaction-model-for-recommendation-on-heterogeneous-graph An efficient neighborhood-based interaction model for recommendation on heterogeneous graph]<br /> # {{bandits}} [https://dl.acm.org/doi/10.1145/3097983.3098184 An Efficient Bandit Algorithm for Realtime Multivariate Optimization], [[KDD 2017]]<br /> # {{neural}} [https://www.amazon.science/publications/the-effectiveness-of-a-two-layer-neural-network-for-recommendations The Effectiveness of a Two-layer Neural Network for Recommendations], [[ICLR 2018]]<br /> # {{bandits}} [https://arxiv.org/pdf/1810.01859.pdf Contextual Multi-Armed Bandits for Causal Marketing], [[ICML 2018]]<br /> # [https://dl.acm.org/doi/pdf/10.1145/3219819.3219891 Buy It Again: Modeling Repeat Purchase Recommendations], [[KDD 2018]]<br /> # [https://www.amazon.science/publications/lore-a-large-scale-offer-recommendation-engine-through-the-lens-of-an-online-subscription-service LORE: A Large-Scale Offer Recommendation Engine Through the Lens of an Online Subscription Service]<br /> # {{search}} [https://www.amazon.science/publications/learning-robust-models-for-e-commerce-product-search Learning Robust Models for e-Commerce Product Search]<br /> # {{search}} [https://www.amazon.science/publications/treating-cold-start-in-product-search-by-priors Treating Cold Start in Product Search by Priors]<br /> # {{performance}} [https://www.amazon.science/publications/scalable-feature-selection-for-multitask-gradient-boosted-trees Scalable Feature Selection for (Multitask) Gradient Boosted Trees]<br /> # {{ltor}} [https://www.amazon.science/publications/multi-objective-relevance-ranking Multi-objective Relevance Ranking via Constrained Optimization]<br /> # [https://www.amazon.science/publications/search-defects-classification-in-e-commerce-platforms-using-language-agnostic-representation-learning Language-Agnostic Representation Learning for Product Search on E-Commerce Platforms]<br /> # {{page composition}} [https://www.amazon.science/publications/whole-page-optimization-with-local-and-global-constraints Whole page optimization with local and global constraints], [[KDD 2019]] (Amazon Video)<br /> # {{performance}} [https://arxiv.org/pdf/1901.04321.pdf Large-scale Collaborative Filtering with Product Embeddings], 2019<br /> # [https://www.amazon.science/publications/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation P-Companion: A principled framework for diversified complementary product recommendation], [[CIKM 2020]]<br /> # {{search}} [https://www.amazon.science/publications/why-do-people-buy-irrelevant-items-in-voice-product-search Why Do People Buy Seemingly Irrelevant Items in Voice Product Search?], [[WSDM 2020]], [https://www.amazon.science/blog/why-do-customers-buy-seemingly-irrelevant-products blog post]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3394486.3403278 Temporal-Contextual Recommendation in Real-Time], [[KDD 2020]] (best applied data science paper), [https://docs.google.com/document/d/11Q5DN4QtzXUrU_aZJDw2gvmKm0OjwMwrBhrHfIm84oc/edit# notes]<br /> # [https://www.amazon.science/publications/challenges-and-research-opportunities-in-ecommerce-search-and-recommendations Challenges and research opportunities in ecommerce search and recommendations], SIGIR Forum 2020<br /> # [https://www.amazon.science/publications/a-flexible-large-scale-similar-product-identification-system-in-e-commerce A flexible large-scale similar product identification system in e-commerce], [[KDD 1st International Workshop on Industrial Recommendation 2020]]<br /> # {{ltor}} [https://www.amazon.science/publications/cpr-collaborative-pairwise-ranking-for-online-list-recommendations CPR: Collaborative pairwise ranking for online list recommendations], [[RecSys 2020 Workshop on Online Recommender Systems and User Modeling]]<br /> # {{fashion}} [https://www.amazon.science/publications/fashion-outfit-complementary-item-retrieval Fashion Outfit Complementary Item Retrieva], [[CVPR 2020]]<br /> # [https://arxiv.org/pdf/2012.08489.pdf Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization], 2020<br /> # [https://arxiv.org/pdf/2012.06678.pdf TabTransformer: Tabular Data Modeling Using Contextual Embeddings], arXiv preprint, 2020<br /> # {{bandits}} [https://www.amazon.science/publications/learning-from-extreme-bandit-feedback Learning from eXtreme bandit feedback]<br /> # {{neural}} [https://www.amazon.science/publications/heterogeneous-graph-neural-networks-with-neighbor-sim-attention-mechanism-for-substitute-product-recommendation Heterogeneous graph neural networks with neighbor-SIM attention mechanism for substitute product recommendation], DLG-AAAI 2021<br /> # {{search}} [https://www.amazon.science/publications/seasonal-relevance-in-e-commerce-search Seasonal relevance in e-commerce search], [[CIKM 2021]]<br /> # {{fashion}} [https://arxiv.org/pdf/2207.12033.pdf Contrastive Learning for Interactive Recommendation in Fashion], [[SIGIR 2022]]<br /> # {{search}}{{experimentation}} N. Bi, P. Castells, D. Gilbert, S. Galperin, P. Tardif, S. Ahuja: [https://www.amazon.science/publications/debiased-balanced-interleaving-at-amazon-search Debiased balanced interleaving at Amazon Search], [[CIKM 2022]]<br /> # [https://pages.cs.wisc.edu/~hous21/papers/UAI23.pdf A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models], [[UAI 2023]]<br /> # [https://pages.cs.wisc.edu/~hous21/papers/KDD23.pdf Neural Insights for Digital Marketing Content Design], [[KDD 2023]]<br /> <br /> == Blog posts ==<br /> <br /> * [https://www.amazon.science/blog/applying-pecos-to-product-retrieval-and-text-autocompletion Applying PECOS to product retrieval and text autocompletion], 2021-08-26<br /> * {{neural}} {{performance}} [https://www.amazon.science/blog/how-to-train-large-graph-neural-networks-efficiently How to train large graph neural networks efficiently], 2021-08-20<br /> * {{production}} [https://www.amazon.science/latest-news/the-science-behind-amazons-new-stylesnap-for-home-feature The science behind Amazon’s new StyleSnap for Home feature], 2020-12-22<br /> * [https://www.amazon.science/latest-news/amazon-scholar-george-karypis-receives-icdm-10-year-highest-impact-award George Karypis receives ICDM 10-Year-Highest-Impact award] (about SLIM), 2020-12-08<br /> * [https://aws.amazon.com/blogs/media/whats-new-in-recommender-systems/ What’s new in recommender systems], 2020-11-17<br /> * {{bandits}} [https://www.amazon.science/blog/a-general-approach-to-solving-bandit-problems A general approach to solving bandit problems], 2020-10<br /> * [https://www.amazon.science/the-history-of-amazons-recommendation-algorithm The history of Amazon’s recommendation algorithm], 2019-11-22<br /> * [https://www.amazon.science/blog/improving-complementary-product-recommendations Improving complementary-product recommendations]<br /> * [https://www.amazon.science/blog/cvpr-deep-learning-has-more-gas-in-the-tank CVPR: Deep learning has more gas in the tank]<br /> * [https://www.amazon.science/conferences-and-events/cvpr-2020 Amazon publications at CVPR 2020]<br /> * {{visual}} [https://www.amazon.science/blog/how-computer-vision-will-help-amazon-customers-shop-online How computer vision will help Amazon customers shop online]<br /> <br /> == Articles ''about'' Amazon ==<br /> <br /> # {{search}} [https://www.vox.com/2018/9/10/17797720/amazon-is-stuffing-its-search-results-pages-with-ads Amazon is stuffing its search results pages with ads]<br /> <br /> == Software ==<br /> * https://github.com/amzn/amazon-dsstne: open source software library for training and deploying recommendation models with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices that are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon to generate personalized product recommendations for our customers at Amazon’s scale.<br /> <br /> == External links ==<br /> <br /> * http://www.amazon.com/<br /> * [https://www.amazon.science/ science blog]<br /> * [https://aws.amazon.com/personalize/ AWS Personalize]<br /> * [https://github.com/amzn Amazon GitHub]<br /> * [https://github.com/aws AWS GitHub]<br /> * [https://github.com/awslabs awslabs GitHub]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2677 2023-07-02T21:07:36Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[Yandex]] [https://openreview.net/pdf?id=MXfTQp8bZF]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alphabet&diff=2676 2023-06-27T11:58:06Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alphabet''' is the parent company of '''Google''' and many of its (former) subsidiaries, for example '''YouTube''' and '''DeepMind'''.<br /> <br /> <br /> == Tutorials ==<br /> <br /> # [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning]<br /> # [https://developers.google.com/machine-learning/recommendation 4-hour tutorial on Recommendation Systems]<br /> <br /> == Papers ==<br /> <br /> # [https://dl.acm.org/doi/pdf/10.1145/3600097 On Reducing User Interaction Data for Personalization], [[ACM TORS]], 2023<br /> # [https://arxiv.org/pdf/2306.01720.pdf Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation], [[KDD 2023]]<br /> # [https://arxiv.org/abs/2305.06474 Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction], ??? 2023<br /> # [https://arxiv.org/abs/2305.05065 Recommender Systems with Generative Retrieval], ??? 2023<br /> # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022<br /> # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]]<br /> # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022<br /> # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]]<br /> # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]]<br /> # {{ltor}}{{neural}} [https://openreview.net/pdf/033e77d13245fbc5492689e8b06afbfd433384d8.pdf Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?], [[ICLR 2021]]<br /> # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]]<br /> # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias<br /> # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems]<br /> # {{bandits}} [[https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html|Latent Bandits Revisited]], [[NeurIPS 2020]]<br /> # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]]<br /> # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2005.02553 Interpretable Learning-to-Rank with Generalized Additive Models], 2020<br /> # [https://dl.acm.org/doi/abs/10.1145/3366423.3380130 Off-policy Learning in Two-stage Recommender Systems], [[WWW 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3366424.3386195 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations], [[WWW 2020]]; Google Play<br /> # {{bias}} [https://research.google/pubs/pub49273/ Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies], [[KDD 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3383313.3412488 Neural Collaborative Filtering vs. Matrix Factorization Revisited], [[RecSys 2020]]<br /> # [https://arxiv.org/abs/2008.02930 Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval], [[CIKM 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2008.13535 DCN V2: Improved Deep &amp;amp; Cross Network and Practical Lessons for Web-scale Learning to Rank Systems], 2020<br /> # {{runtime}} [https://arxiv.org/abs/1908.10396 Accelerating Large-Scale Inference with Anisotropic Vector Quantization], 2019<br /> # [https://arxiv.org/pdf/1905.12767.pdf Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology], 2019<br /> # [https://arxiv.org/pdf/1810.02019.pdf Seq2Slate: Re-ranking and Slate Optimization with RNNs], 2019<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3331184.3331347 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks], [[SIGIR 2019]] (short paper)<br /> # {{neural}}[https://research.google/pubs/pub47954/ Towards Neural Mixture Recommender for Long Range Dependent User Sequences], [[WWW 2019]]<br /> # [https://arxiv.org/abs/1812.02353 Top-K Off-Policy Correction for a REINFORCE Recommender System], [[WSDM 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1902.08588 Towards neural mixture recommender for long range dependent user sequences], [[WWW 2019]]<br /> # {{ltor}} [https://daiwk.github.io/assets/youtube-multitask.pdf Recommending what video to watch next: A multitask ranking system], [[RecSys 2019]]<br /> # {{bias}} [https://research.google/pubs/pub48840/ Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations], [[RecSys 2019]]<br /> # [https://research.google/pubs/pub47705/ Efficient Training on Very Large Corpora via Gramian Estimation], [[ICLR 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3159652.3159727 Latent Cross: Making Use of Context in Recurrent Recommender Systems], [[WSDM 2018]]<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], [[KDD 2018]]<br /> # [https://research.google/pubs/pub47630/ Categorical-Attributes-Based Multi-Level Classification for Recommender Systems], [[RecSys 2018]]<br /> # {{neural}}{{bandits}} [https://openreview.net/pdf?id=SyYe6k-CW Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling], [[ICLR 2018]]<br /> # {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3269206.3271784 The LambdaLoss Framework for Ranking Metric Optimization], [[CIKM 2018]]<br /> # {{production}}{{experimentation}} ''[https://dl.acm.org/ft_gateway.cfm?id=3272018&amp;type=pdf Practical Diversified Recommendations on YouTube with Determinantal Point Processes]'', [[CIKM 2018]]<br /> # [https://datalab.csd.auth.gr/~gounaris/icde2018-google-recommendations.pdf Recommendations for All: Solving Thousands of Recommendation Problems Daily], ICDE 2018 (also describe user context representation by the actions taken)<br /> # [https://research.google/pubs/pub46300/ A Generic Coordinate Descent Framework for Learning from Implicit Feedback], [[WWW 2017]]<br /> # {{neural}} [https://arxiv.org/pdf/1708.05123.pdf Deep &amp;amp; Cross Network for Ad Click Predictions], 2017<br /> # {{production}} [https://dl.acm.org/ft_gateway.cfm?id=2959190&amp;type=pdf Deep neural networks for YouTube recommendations], [[RecSys 2016]]; [https://dl.acm.org/doi/abs/10.1145/2959100.2959190 video]<br /> # {{production}}{{neural}} [https://arxiv.org/abs/1606.07792 Wide &amp;amp; Deep Learning for Recommender Systems], DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], KDD 2018 (Google)<br /> # [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf Towards Conversational Recommender Systems], [[KDD 2016]]<br /> # {{ads}} [https://dl.acm.org/ft_gateway.cfm?id=2788583&amp;type=pdf Focusing on the Long-term: It’s Good for Users and Business], [[KDD 2015]]<br /> # {{ads}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41159.pdf Ad Click Prediction: a View from the Trenches], [[KDD 2013]]<br /> # {{production}} [https://tsinghua-nslab.github.io/seminar/2012Spring/11_3/YouTube_RecSys10.pdf The YouTube video recommendation system], [[RecSys 2010]]<br /> # [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.4329&amp;rep=rep1&amp;type=pdf Google news personalization: scalable online collaborative filtering], [[WWW 2007]]<br /> <br /> == Talks ==<br /> <br /> # [https://slideslive.com/38917655/reinforcement-learning-in-recommender-systems-some-challenges Reinforcement Learning for Recommender Systems: Some Challenges], ICML 2019<br /> <br /> == Blog posts ==<br /> <br /> # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07<br /> # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21<br /> # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12<br /> # [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html Announcing ScaNN: Efficient Vector Similarity Search], 2020-07-28<br /> # [https://deepmind.com/blog/article/Advanced-machine-learning-helps-Play-Store-users-discover-personalised-apps Advanced machine learning helps Play Store users discover personalised apps], 2019-11-18<br /> <br /> == Software ==<br /> <br /> # [https://github.com/google/trax Trax]: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to [https://github.com/tensorflow/tensor2tensor tensor2tensor].<br /> # [https://github.com/google/rax rax], learning-to-rank framework for JAX, [https://research.google/pubs/pub51453/ paper]<br /> # [https://github.com/google-research/recsim RecSim] [https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html blog post]<br /> # [https://github.com/google-research/google-research/tree/master/scann ScaNN (Scalable Nearest Neighbors)]<br /> # [https://www.tensorflow.org/recommenders TensorFlow Recommenders] see also [https://github.com/tensorflow/community/blob/master/sigs/recommenders/CHARTER.md TF Recommenders SIG] and [https://github.com/tensorflow/recommenders-addons TF recommender add-ons]<br /> # [https://github.com/tensorflow/ranking TensorFlow Ranking]<br /> <br /> == External link ==<br /> <br /> * https://abc.xyz/<br /> * [https://en.wikipedia.org/wiki/Alphabet_Inc. Wikipedia article about Alphabet]<br /> * GitHub repositories:<br /> ** [https://github.com/google/ Google]<br /> ** [https://github.com/google-research/ Google Research] -- Google Research has a [https://github.com/google-research/google-research mono-repo] with most of their projects.<br /> ** [https://github.com/deepmind DeepMind]<br /> ** [https://github.com/youtube YouTube]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alphabet&diff=2675 2023-06-21T13:40:32Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alphabet''' is the parent company of '''Google''' and many of its (former) subsidiaries, for example '''YouTube''' and '''DeepMind'''.<br /> <br /> <br /> == Tutorials ==<br /> <br /> # [https://developers.google.com/machine-learning/guides/rules-of-ml Rules of Machine Learning]<br /> # [https://developers.google.com/machine-learning/recommendation 4-hour tutorial on Recommendation Systems]<br /> <br /> == Papers ==<br /> <br /> # {{ops}} [https://research.google/pubs/pub51712/ Data Management Principles], book chapter in ''Reliable Machine Learning: Applying SRE Principles to ML in Production'', 2022<br /> # [https://research.google/pubs/pub51652/ Surrogate for Long-Term User Experience in Recommender Systems], [[KDD 2022]]<br /> # {{neural}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/827afbd792b84f20bf1b439d1d678e121c9cfa46.pdf Scale Calibration of Deep Ranking Models], KDD 2022<br /> # [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/bfbc205383e9fc0aa132011c587d5f826ba90274.pdf Bootstrapping Recommendations at Chrome Web Store], [[KDD 2021]]<br /> # {{ops}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/0d556e45afc54afeb2eb6b51a9bc1827b9961ff4.pdf “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI], [[CHI 2021]]<br /> # {{ltor}}{{neural}} [https://openreview.net/pdf/033e77d13245fbc5492689e8b06afbfd433384d8.pdf Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?], [[ICLR 2021]]<br /> # {{explanations}} [https://dl.acm.org/doi/pdf/10.1145/3397271.3401032 Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations], [[RecSys 2021]]<br /> # [https://arxiv.org/pdf/2101.08769.pdf Item Recommendation from Implicit Feedback], 2021-01-21 – write-up on item recommendation from positive-only feedback with a focus on algorithms; no experiments, no dealing with bias<br /> # [https://research.google/pubs/pub49284/ Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems]<br /> # {{bandits}} [[https://proceedings.neurips.cc/paper/2020/hash/9b7c8d13e4b2f08895fb7bcead930b46-Abstract.html|Latent Bandits Revisited]], [[NeurIPS 2020]]<br /> # [https://papers.nips.cc/paper/2020/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf Rankmax: An Adaptive Projection Alternative to the Softmax Function], [[NeurIPS 2020]]<br /> # {{performance}} [http://proceedings.mlr.press/v108/han20b.html MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search], [[AISTATS 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2005.02553 Interpretable Learning-to-Rank with Generalized Additive Models], 2020<br /> # [https://dl.acm.org/doi/abs/10.1145/3366423.3380130 Off-policy Learning in Two-stage Recommender Systems], [[WWW 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3366424.3386195 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations], [[WWW 2020]]; Google Play<br /> # {{bias}} [https://research.google/pubs/pub49273/ Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies], [[KDD 2020]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3383313.3412488 Neural Collaborative Filtering vs. Matrix Factorization Revisited], [[RecSys 2020]]<br /> # [https://arxiv.org/abs/2008.02930 Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval], [[CIKM 2020]]<br /> # {{ltor}} [https://arxiv.org/abs/2008.13535 DCN V2: Improved Deep &amp;amp; Cross Network and Practical Lessons for Web-scale Learning to Rank Systems], 2020<br /> # {{runtime}} [https://arxiv.org/abs/1908.10396 Accelerating Large-Scale Inference with Anisotropic Vector Quantization], 2019<br /> # [https://arxiv.org/pdf/1905.12767.pdf Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology], 2019<br /> # [https://arxiv.org/pdf/1810.02019.pdf Seq2Slate: Re-ranking and Slate Optimization with RNNs], 2019<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3331184.3331347 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks], [[SIGIR 2019]] (short paper)<br /> # {{neural}}[https://research.google/pubs/pub47954/ Towards Neural Mixture Recommender for Long Range Dependent User Sequences], [[WWW 2019]]<br /> # [https://arxiv.org/abs/1812.02353 Top-K Off-Policy Correction for a REINFORCE Recommender System], [[WSDM 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1902.08588 Towards neural mixture recommender for long range dependent user sequences], [[WWW 2019]]<br /> # {{ltor}} [https://daiwk.github.io/assets/youtube-multitask.pdf Recommending what video to watch next: A multitask ranking system], [[RecSys 2019]]<br /> # {{bias}} [https://research.google/pubs/pub48840/ Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations], [[RecSys 2019]]<br /> # [https://research.google/pubs/pub47705/ Efficient Training on Very Large Corpora via Gramian Estimation], [[ICLR 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/10.1145/3159652.3159727 Latent Cross: Making Use of Context in Recurrent Recommender Systems], [[WSDM 2018]]<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], [[KDD 2018]]<br /> # [https://research.google/pubs/pub47630/ Categorical-Attributes-Based Multi-Level Classification for Recommender Systems], [[RecSys 2018]]<br /> # {{neural}}{{bandits}} [https://openreview.net/pdf?id=SyYe6k-CW Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling], [[ICLR 2018]]<br /> # {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3269206.3271784 The LambdaLoss Framework for Ranking Metric Optimization], [[CIKM 2018]]<br /> # {{production}}{{experimentation}} ''[https://dl.acm.org/ft_gateway.cfm?id=3272018&amp;type=pdf Practical Diversified Recommendations on YouTube with Determinantal Point Processes]'', [[CIKM 2018]]<br /> # [https://datalab.csd.auth.gr/~gounaris/icde2018-google-recommendations.pdf Recommendations for All: Solving Thousands of Recommendation Problems Daily], ICDE 2018 (also describe user context representation by the actions taken)<br /> # [https://research.google/pubs/pub46300/ A Generic Coordinate Descent Framework for Learning from Implicit Feedback], [[WWW 2017]]<br /> # {{neural}} [https://arxiv.org/pdf/1708.05123.pdf Deep &amp;amp; Cross Network for Ad Click Predictions], 2017<br /> # {{production}} [https://dl.acm.org/ft_gateway.cfm?id=2959190&amp;type=pdf Deep neural networks for YouTube recommendations], [[RecSys 2016]]; [https://dl.acm.org/doi/abs/10.1145/2959100.2959190 video]<br /> # {{production}}{{neural}} [https://arxiv.org/abs/1606.07792 Wide &amp;amp; Deep Learning for Recommender Systems], DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play<br /> # [http://www.alexbeutel.com/papers/q-and-r-kdd2018.pdf Q&amp;amp;R: A two-stage approach toward interactive recommendation], KDD 2018 (Google)<br /> # [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/rfp0063-christakopoulou.pdf Towards Conversational Recommender Systems], [[KDD 2016]]<br /> # {{ads}} [https://dl.acm.org/ft_gateway.cfm?id=2788583&amp;type=pdf Focusing on the Long-term: It’s Good for Users and Business], [[KDD 2015]]<br /> # {{ads}} [https://storage.googleapis.com/pub-tools-public-publication-data/pdf/41159.pdf Ad Click Prediction: a View from the Trenches], [[KDD 2013]]<br /> # {{production}} [https://tsinghua-nslab.github.io/seminar/2012Spring/11_3/YouTube_RecSys10.pdf The YouTube video recommendation system], [[RecSys 2010]]<br /> # [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.4329&amp;rep=rep1&amp;type=pdf Google news personalization: scalable online collaborative filtering], [[WWW 2007]]<br /> <br /> == Talks ==<br /> <br /> # [https://slideslive.com/38917655/reinforcement-learning-in-recommender-systems-some-challenges Reinforcement Learning for Recommender Systems: Some Challenges], ICML 2019<br /> <br /> == Blog posts ==<br /> <br /> # [https://cloud.google.com/blog/topics/developers-practitioners/building-large-scale-recommenders-using-cloud-tpus Building Large Scale Recommenders using Cloud TPUs], 2022-10-07<br /> # [https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-Ranking], 2021-07-21<br /> # [https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html Scholar Recommendations Reloaded! Fresher, More Relevant, Easier], 2021-02-12<br /> # [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html Announcing ScaNN: Efficient Vector Similarity Search], 2020-07-28<br /> # [https://deepmind.com/blog/article/Advanced-machine-learning-helps-Play-Store-users-discover-personalised-apps Advanced machine learning helps Play Store users discover personalised apps], 2019-11-18<br /> <br /> == Software ==<br /> <br /> # [https://github.com/google/trax Trax]: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to [https://github.com/tensorflow/tensor2tensor tensor2tensor].<br /> # [https://github.com/google/rax rax], learning-to-rank framework for JAX, [https://research.google/pubs/pub51453/ paper]<br /> # [https://github.com/google-research/recsim RecSim] [https://ai.googleblog.com/2019/11/recsim-configurable-simulation-platform.html blog post]<br /> # [https://github.com/google-research/google-research/tree/master/scann ScaNN (Scalable Nearest Neighbors)]<br /> # [https://www.tensorflow.org/recommenders TensorFlow Recommenders] see also [https://github.com/tensorflow/community/blob/master/sigs/recommenders/CHARTER.md TF Recommenders SIG] and [https://github.com/tensorflow/recommenders-addons TF recommender add-ons]<br /> # [https://github.com/tensorflow/ranking TensorFlow Ranking]<br /> <br /> == External link ==<br /> <br /> * https://abc.xyz/<br /> * [https://en.wikipedia.org/wiki/Alphabet_Inc. Wikipedia article about Alphabet]<br /> * GitHub repositories:<br /> ** [https://github.com/google/ Google]<br /> ** [https://github.com/google-research/ Google Research] -- Google Research has a [https://github.com/google-research/google-research mono-repo] with most of their projects.<br /> ** [https://github.com/deepmind DeepMind]<br /> ** [https://github.com/youtube YouTube]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2674 2023-06-21T13:39:35Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2673 2023-05-23T10:33:26Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2672 2023-05-23T10:31:42Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2671 2023-05-23T10:31:14Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2670 2023-05-23T10:30:01Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2669 2023-05-23T10:29:47Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * [[Etsy]]<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Google]] -&gt; [[Alphabet]]<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * [[Kaggle]]<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * [[Myrrix]]<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Meta&diff=2668 2023-05-22T15:40:46Z

<p>Zeno Gantner: added and fixed blog post links</p> <hr /> <div>'''Meta''' is the company behind '''Facebook''', '''Instagram''', and '''WhatsApp'''.<br /> <br /> == Blog posts ==<br /> <br /> # [https://engineering.fb.com/2022/10/31/ml-applications/instagram-notification-management-machine-learning/ Improving Instagram notification management with machine learning and causal inference], 2022-10-31<br /> # [https://ai.facebook.com/blog/facebook-feed-improvements-ai-show-more-less/ The new AI-powered feature designed to improve Feed for everyone], 2022-10-05<br /> # [https://ai.facebook.com/tools/system-cards/instagram-feed-ranking/# What is the Instagram Feed?], 2022-02-23<br /> # [https://research.fb.com/blog/2021/08/when-do-recommender-systems-amplify-user-preferences-a-theoretical-framework-and-mitigation-strategies/ When do recommender systems amplify user preferences? A theoretical framework and mitigation strategies], 2021-08<br /> # [https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works Shedding More Light on How Instagram Works], 2021-06-08<br /> # [https://engineering.fb.com/2020/12/17/ml-applications/diversified-recommendations/ On the value of diversified recommendations], 2020-12-17<br /> # [https://engineering.fb.com/2020/12/10/web/how-instagram-suggests-new-content/ How Instagram suggests new content], 2020-12-10<br /> # [https://about.instagram.com/blog/engineering/designing-a-constrained-exploration-system Designing a Constrained Exploration System], 2020-12-10<br /> # [https://instagram-engineering.com/five-things-i-learned-about-working-on-content-quality-at-instagram-5031b1342bea Five things I learned about working on content quality at Instagram], 2020-01-25<br /> # {{production}} [https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882 Instagram’s Explore Recommender System], 2019-11-26 [https://news.ycombinator.com/item?id=21638216 HackerNews discussion]<br /> #* 3-part funnel (2 layers of [[candidate generation]])<br /> #* domain-specific language (We have separation between model and filter config).<br /> #* account embeddings<br /> #* embedding-based<br /> #* “See fewer posts like this” – [[explicit feedback]]<br /> # [https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15 Offline Policy Evaluation: Run fewer, better A/B tests]<br /> # [https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/ DLRM: An advanced, open source deep learning recommendation model], 2019-07-02<br /> # [https://instagram-engineering.com/lessons-learned-at-instagram-stories-and-feed-machine-learning-54f3aaa09e56 Lessons Learned at Instagram Stories and Feed Machine Learning], 2018-12-18<br /> # [https://research.facebook.com/blog/2018/9/efficient-tuning-of-online-systems-using-bayesian-optimization/ Efficient tuning of online systems using Bayesian optimization], 2018-09-17<br /> <br /> == Papers ==<br /> <br /> # {{ops}}{{performance}} [https://arxiv.org/pdf/2104.05158.pdf Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models], ISCA 2022 industry track<br /> # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]<br /> # {{ads}}{{neural}} [https://arxiv.org/pdf/2203.11014.pdf DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction], KDD 2022<br /> # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021<br /> # [https://research.fb.com/publications/preference-amplification-in-recommender-systems/ Preference Amplification in Recommender Systems], [[KDD 2021]]<br /> # {{search}} [https://www.kdd.org/kdd2020/accepted-papers/view/embedding-based-retrieval-in-facebook-search Embedding-based Retrieval in Facebook Search], [[KDD 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/compositional-embeddings-using-complementary-partitions-for-memory-efficien Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems], [[KDD 2020]]<br /> # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020<br /> # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)<br /> # {{ads}} [https://research.fb.com/publications/counterfactual-reasoning-and-learning-systems-the-example-of-computational-advertising/ Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising]<br /> # [https://arxiv.org/abs/1909.11810 Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems]<br /> # {{ads}} [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014<br /> # [https://cs.stanford.edu/~jure/pubs/linkpred-wsdm11.pdf Supervised Random Walks: Predicting and Recommending Links in Social Networks], [[WSDM 2011]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/facebookresearch/dlrm DLRM recommender]: click probability model<br /> # [https://github.com/facebook/prophet Prophet]: forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.<br /> # [https://github.com/facebook/FAI-PEP Facebook AI Performance Evaluation Platform]: framework and backend agnostic benchmarking platform to compare machine learning inferencing runtime metrics on a set of models and on variety of backends.<br /> # {{ltor}} [https://github.com/facebookresearch/StarSpace StarSpace]: Learning embeddings for classification, retrieval and ranking.<br /> # [https://github.com/facebookresearch/ReAgent ReAgent/Horizon]: end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. [https://reagent.ai/rasp_tutorial.html# Tutorial] contains e-commerce/recommendation example. [https://research.fb.com/publications/horizon-facebooks-open-source-applied-reinforcement-learning-platform/ paper]<br /> # [https://github.com/facebookresearch/faiss faiss]: library for efficient similarity search and clustering of dense vectors.<br /> # [https://github.com/facebookresearch/pysparnn pysparnn]: approximate nearest neighbor search for sparse data in Python.<br /> # [https://github.com/facebook/Ax Ax]: adaptive experimentation platform, [https://ax.dev/ ax.dev]<br /> <br /> == External links ==<br /> <br /> * [https://research.fb.com/blog/ research blog]<br /> * [https://research.fb.com/publications/ research publications]<br /> * [https://engineering.fb.com/ tech blog]<br /> * [https://instagram-engineering.com old Instagram tech blog]<br /> * GitHub repositories<br /> ** [https://github.com/facebook Facebook]<br /> ** [https://github.com/facebookresearch FB Research]<br /> ** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]<br /> ** [https://github.com/Instagram Instagram]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Amazon&diff=2667 2023-05-22T12:59:17Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Amazon''' is the largest online retailer as well, with its subsidiary Amazon Web Services (AWS), the largest cloud provider in the Western world.<br /> They were also one of the first, if not the first, commercial user of recommendation systems.<br /> AWS also offers [[recommendations as a service]] with their product [[AWS Personalize]].<br /> <br /> <br /> == Papers ==<br /> <br /> # [https://www.amazon.science/publications?f0=0000016e-2ff2-d205-a5ef-affb543e0000&amp;s=0 all search and information retrieval publications by Amazon]<br /> # [http://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Amazon-Recommendations.pdf Amazon.com Recommendations: Item-to-Item Collaborative Filtering], IEEE Internet Computing, 2003<br /> # [https://dl.acm.org/doi/pdf/10.1145/2764468.2764488 Estimating the Causal Impact of Recommendation Systems from Observational Data], EC 2015<br /> # [https://dl.acm.org/doi/10.1145/2783258.2788579 One-Pass Ranking Models for Low-Latency Product Recommendations], KDD 2015<br /> # {{visual}} [https://dl.acm.org/doi/10.1145/2959100.2959171 Adaptive, Personalized Diversity for Visual Discovery], RecSys 2016 (best short paper)<br /> # [https://www.amazon.science/publications/diversifying-music-recommendations Diversifying Music Recommendations], ICML 2016<br /> # [https://www.amazon.science/publications/sustainability-at-scale-towards-bridging-the-intention-behavior-gap-with-sustainable-recommendations Sustainability at Scale: Towards Bridging the Intention-Behavior Gap with Sustainable Recommendations], RecSys 2017<br /> # [https://www.amazon.science/publications/recommending-product-sizes-to-customers Recommending Product Sizes to Customers], RecSys 2017<br /> # {{production}} [https://www.amazon.science/publications/two-decades-of-recommender-systems-at-amazon-com Two Decades of Recommender Systems at Amazon.com], 2017<br /> # [https://www.amazon.science/publications/intent-based-relevance-estimation-from-click-logs Intent Based Relevance Estimation from Click Logs], CIKM 2017<br /> # [https://www.amazon.science/publications/mrnet-product2vec-a-multi-task-recurrent-neural-network-for-product-embeddings MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings], ECML-PKDD 2017<br /> # {{ltor}}{{bandits}} [https://arxiv.org/abs/2004.13106 Learning to Rank in the Position Based Model with Bandit Feedback] (Amazon Music)<br /> # {{bandits}} [https://arxiv.org/abs/2004.13576 A Linear Bandit for Seasonal Environments] (Amazon Music)<br /> # [https://www.amazon.science/publications/an-efficient-neighborhood-based-interaction-model-for-recommendation-on-heterogeneous-graph An efficient neighborhood-based interaction model for recommendation on heterogeneous graph]<br /> # {{visual}} [https://dl.acm.org/doi/pdf/10.1145/2959100.2959171 Adaptive, personalized diversity for visual discovery], RecSys 2016<br /> # {{neural}} [https://www.amazon.science/publications/the-effectiveness-of-a-two-layer-neural-network-for-recommendations The Effectiveness of a Two-layer Neural Network for Recommendations], [[ICLR 2018]]<br /> # [https://dl.acm.org/doi/pdf/10.1145/3219819.3219891 Buy It Again: Modeling Repeat Purchase Recommendations], [[KDD 2018]]<br /> # [https://www.amazon.science/publications/lore-a-large-scale-offer-recommendation-engine-through-the-lens-of-an-online-subscription-service LORE: A Large-Scale Offer Recommendation Engine Through the Lens of an Online Subscription Service]<br /> # {{search}} [https://www.amazon.science/publications/learning-robust-models-for-e-commerce-product-search Learning Robust Models for e-Commerce Product Search]<br /> # {{search}} [https://www.amazon.science/publications/treating-cold-start-in-product-search-by-priors Treating Cold Start in Product Search by Priors]<br /> # {{performance}} [https://www.amazon.science/publications/scalable-feature-selection-for-multitask-gradient-boosted-trees Scalable Feature Selection for (Multitask) Gradient Boosted Trees]<br /> # {{ltor}} [https://www.amazon.science/publications/multi-objective-relevance-ranking Multi-objective Relevance Ranking via Constrained Optimization]<br /> # [https://www.amazon.science/publications/search-defects-classification-in-e-commerce-platforms-using-language-agnostic-representation-learning Language-Agnostic Representation Learning for Product Search on E-Commerce Platforms]<br /> # {{page composition}} [https://www.amazon.science/publications/whole-page-optimization-with-local-and-global-constraints Whole page optimization with local and global constraints], [[KDD 2019]] (Amazon Video)<br /> # {{performance}} [https://arxiv.org/pdf/1901.04321.pdf Large-scale Collaborative Filtering with Product Embeddings], 2019<br /> # [https://www.amazon.science/publications/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation P-Companion: A principled framework for diversified complementary product recommendation], [[CIKM 2020]]<br /> # {{search}} [https://www.amazon.science/publications/why-do-people-buy-irrelevant-items-in-voice-product-search Why Do People Buy Seemingly Irrelevant Items in Voice Product Search?], [[WSDM 2020]], [https://www.amazon.science/blog/why-do-customers-buy-seemingly-irrelevant-products blog post]<br /> # {{neural}} [https://dl.acm.org/doi/pdf/10.1145/3394486.3403278 Temporal-Contextual Recommendation in Real-Time], [[KDD 2020]] (best applied data science paper), [https://docs.google.com/document/d/11Q5DN4QtzXUrU_aZJDw2gvmKm0OjwMwrBhrHfIm84oc/edit# notes]<br /> # [https://www.amazon.science/publications/challenges-and-research-opportunities-in-ecommerce-search-and-recommendations Challenges and research opportunities in ecommerce search and recommendations], SIGIR Forum 2020<br /> # [https://www.amazon.science/publications/a-flexible-large-scale-similar-product-identification-system-in-e-commerce A flexible large-scale similar product identification system in e-commerce], [[KDD 1st International Workshop on Industrial Recommendation 2020]]<br /> # {{ltor}} [https://www.amazon.science/publications/cpr-collaborative-pairwise-ranking-for-online-list-recommendations CPR: Collaborative pairwise ranking for online list recommendations], [[RecSys 2020 Workshop on Online Recommender Systems and User Modeling]]<br /> # {{fashion}} [https://www.amazon.science/publications/fashion-outfit-complementary-item-retrieval Fashion Outfit Complementary Item Retrieva], [[CVPR 2020]]<br /> # [https://arxiv.org/pdf/2012.08489.pdf Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization], 2020<br /> # [https://arxiv.org/pdf/2012.06678.pdf TabTransformer: Tabular Data Modeling Using Contextual Embeddings], arXiv preprint, 2020<br /> # {{bandits}} [https://www.amazon.science/publications/learning-from-extreme-bandit-feedback Learning from eXtreme bandit feedback]<br /> # {{neural}} [https://www.amazon.science/publications/heterogeneous-graph-neural-networks-with-neighbor-sim-attention-mechanism-for-substitute-product-recommendation Heterogeneous graph neural networks with neighbor-SIM attention mechanism for substitute product recommendation], DLG-AAAI 2021<br /> # {{search}} [https://www.amazon.science/publications/seasonal-relevance-in-e-commerce-search Seasonal relevance in e-commerce search], [[CIKM 2021]]<br /> # {{fashion}} [https://arxiv.org/pdf/2207.12033.pdf Contrastive Learning for Interactive Recommendation in Fashion], [[SIGIR 2022]]<br /> # {{search}}{{experimentation}} N. Bi, P. Castells, D. Gilbert, S. Galperin, P. Tardif, S. Ahuja: [https://www.amazon.science/publications/debiased-balanced-interleaving-at-amazon-search Debiased balanced interleaving at Amazon Search], [[CIKM 2022]]<br /> <br /> == Blog posts ==<br /> <br /> * [https://www.amazon.science/blog/applying-pecos-to-product-retrieval-and-text-autocompletion Applying PECOS to product retrieval and text autocompletion], 2021-08-26<br /> * {{neural}} {{performance}} [https://www.amazon.science/blog/how-to-train-large-graph-neural-networks-efficiently How to train large graph neural networks efficiently], 2021-08-20<br /> * {{production}} [https://www.amazon.science/latest-news/the-science-behind-amazons-new-stylesnap-for-home-feature The science behind Amazon’s new StyleSnap for Home feature], 2020-12-22<br /> * [https://www.amazon.science/latest-news/amazon-scholar-george-karypis-receives-icdm-10-year-highest-impact-award George Karypis receives ICDM 10-Year-Highest-Impact award] (about SLIM), 2020-12-08<br /> * [https://aws.amazon.com/blogs/media/whats-new-in-recommender-systems/ What’s new in recommender systems], 2020-11-17<br /> * {{bandits}} [https://www.amazon.science/blog/a-general-approach-to-solving-bandit-problems A general approach to solving bandit problems], 2020-10<br /> * [https://www.amazon.science/the-history-of-amazons-recommendation-algorithm The history of Amazon’s recommendation algorithm], 2019-11-22<br /> * [https://www.amazon.science/blog/improving-complementary-product-recommendations Improving complementary-product recommendations]<br /> * [https://www.amazon.science/blog/cvpr-deep-learning-has-more-gas-in-the-tank CVPR: Deep learning has more gas in the tank]<br /> * [https://www.amazon.science/conferences-and-events/cvpr-2020 Amazon publications at CVPR 2020]<br /> * {{visual}} [https://www.amazon.science/blog/how-computer-vision-will-help-amazon-customers-shop-online How computer vision will help Amazon customers shop online]<br /> <br /> == Articles ''about'' Amazon ==<br /> <br /> # {{search}} [https://www.vox.com/2018/9/10/17797720/amazon-is-stuffing-its-search-results-pages-with-ads Amazon is stuffing its search results pages with ads]<br /> <br /> == Software ==<br /> * https://github.com/amzn/amazon-dsstne: open source software library for training and deploying recommendation models with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices that are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon to generate personalized product recommendations for our customers at Amazon’s scale.<br /> <br /> == External links ==<br /> <br /> * http://www.amazon.com/<br /> * [https://www.amazon.science/ science blog]<br /> * [https://aws.amazon.com/personalize/ AWS Personalize]<br /> * [https://github.com/amzn Amazon GitHub]<br /> * [https://github.com/aws AWS GitHub]<br /> * [https://github.com/awslabs awslabs GitHub]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Netflix&diff=2666 2023-05-19T14:03:47Z

<p>Zeno Gantner: Created page with &quot;'''Netflix''' is a video streaming service. The Netflix Prize, announced in 2006, helped to attract a lot of attention to the field of recommender systems. Category:Co...&quot;</p> <hr /> <div>'''Netflix''' is a video streaming service. The [[Netflix Prize]], announced in 2006, helped to attract a lot of attention to the field of recommender systems.<br /> <br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alibaba&diff=2665 2023-05-19T12:38:24Z

<p>Zeno Gantner: </p> <hr /> <div>'''Alibaba''' is a Chinese e-commerce company.<br /> <br /> == Papers ==<br /> <br /> # {{search}} [https://arxiv.org/abs/1805.08524 Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search], [[IJCAI 2018]]<br /> # {{performance}} Wang et al.: [https://arxiv.org/pdf/1803.02349.pdf Billion-scale commodity embedding for e-commerce recommendation in Alibaba], [[KDD 2018]]<br /> # [https://arxiv.org/pdf/1801.02294.pdf Learning Tree-based Deep Model for Recommender Systems], KDD 2018<br /> # [https://arxiv.org/abs/1706.06978 Deep Interest Network for Click-Through Rate Prediction], KDD 2018<br /> # {{search}} {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3298689.3347000?download=true Personalized Re-ranking for Recommendation], [[RecSys 2019]]<br /> # {{neural}} [https://arxiv.org/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], [[AAAI 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1905.06874.pdf Behavior Sequence Transformer for E-commerce Recommendation in Alibaba], [[DLP-KDD workshop 2019]]<br /> # {{neural}} Zhu et al.: Joint Optimization of Tree-based Index and Deep Model for Recommender Systems, [[NIPS 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/abs/10.1145/3357384.3357895 BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer], [[CIKM 2019]]<br /> # [https://ieeexplore.ieee.org/abstract/document/9495161 Co-Displayed Items Aware List Recommendation], IEEE TODO, 2020<br /> # [https://dl.acm.org/doi/10.1145/3383313.3412238 PURS: Personalized Unexpected Recommender System for Improving User Satisfaction], [[RecSys 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/privileged-features-distillation-at-taobao-recommendations Privileged Features Distillation at Taobao Recommendations], [[KDD 2020]]<br /> # {{ltor}} [https://ieeexplore.ieee.org/abstract/document/9495161 AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online], IEEE TKDE 2021<br /> # {{ads}} [https://dl.acm.org/doi/abs/10.1145/3447548.3467086 Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling], [[KDD 2021]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/alibaba/EasyRec EasyRec]<br /> # [https://github.com/alibaba/DeepRec DeepRec]: uses TF 1.15<br /> # [https://github.com/shenweichen/DeepCTR-Torch DeepCTR] (by an Alibaba employee)<br /> # [https://github.com/shenweichen/DeepCTR-Torch DeepCTR-Torch] (by an Alibaba employee)<br /> <br /> == External links ==<br /> * [https://github.com/alibaba GitHub] (436 repos as of 2022-05, at least 2 of them relevant)<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2664 2023-05-19T12:35:43Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR]<br /> * [[Commendo]]<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * Etsy<br /> * &lt;s&gt;[[Facebook]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmaster]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmtipset]]&lt;/s&gt; (thanks Alan)<br /> * &lt;s&gt;[[Flixster]]&lt;/s&gt; (thanks srbecker)<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Google]] -&gt; [[Alphabet]]<br /> * &lt;s&gt;[[Gravity]]&lt;/s&gt;<br /> * &lt;s&gt;[[Hulu]]&lt;/s&gt;<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * &lt;s&gt;'''[[Instagram]]'''&lt;/s&gt;<br /> * [[Kaggle]]<br /> * &lt;s&gt;[[Knewton]]&lt;/s&gt;<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * &lt;s&gt;[[LinkedIn]]&lt;/s&gt;<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * &lt;s&gt;[[Moviepilot]]&lt;/s&gt; (thanks Alan)<br /> * [[Myrrix]]<br /> * '''[[Netflix]]'''<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * &lt;s&gt;[[Plista]]&lt;/s&gt;<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * &lt;s&gt;[[Zalando]]&lt;/s&gt;<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2663 2023-05-19T12:30:33Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * &lt;s&gt;'''[[Alphabet]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Amazon]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Apple]]'''&lt;/s&gt;<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer]<br /> * [[Commendo]]<br /> * &lt;s&gt;'''[[Criteo]]'''&lt;/s&gt;<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * Etsy<br /> * &lt;s&gt;[[Facebook]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmaster]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmtipset]]&lt;/s&gt; (thanks Alan)<br /> * &lt;s&gt;[[Flixster]]&lt;/s&gt; (thanks srbecker)<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Google]] -&gt; [[Alphabet]]<br /> * &lt;s&gt;[[Gravity]]&lt;/s&gt;<br /> * &lt;s&gt;[[Hulu]]&lt;/s&gt;<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * &lt;s&gt;'''[[Instagram]]'''&lt;/s&gt;<br /> * [[Kaggle]]<br /> * &lt;s&gt;[[Knewton]]&lt;/s&gt;<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * &lt;s&gt;[[LinkedIn]]&lt;/s&gt;<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * &lt;s&gt;[[Moviepilot]]&lt;/s&gt; (thanks Alan)<br /> * [[Myrrix]]<br /> * '''[[Netflix]]'''<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * &lt;s&gt;[[Plista]]&lt;/s&gt;<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * &lt;s&gt;[[Zalando]]&lt;/s&gt;<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Meta&diff=2662 2023-05-19T12:09:38Z

<p>Zeno Gantner: </p> <hr /> <div>'''Meta''' is the company behind '''Facebook''', '''Instagram''', and '''WhatsApp'''.<br /> <br /> == Blog posts ==<br /> <br /> # [https://ai.facebook.com/blog/facebook-feed-improvements-ai-show-more-less/ The new AI-powered feature designed to improve Feed for everyone], 2022-10-05<br /> # [https://research.fb.com/blog/2021/08/when-do-recommender-systems-amplify-user-preferences-a-theoretical-framework-and-mitigation-strategies/ When do recommender systems amplify user preferences? A theoretical framework and mitigation strategies], 2021-08<br /> # [https://research.fb.com/efficient-tuning-of-online-systems-using-bayesian-optimization/ Efficient tuning of online systems using Bayesian optimization]<br /> # [https://engineering.fb.com/2020/12/17/ml-applications/diversified-recommendations/ On the value of diversified recommendations], 2020-12-17<br /> # [https://engineering.fb.com/2020/12/10/web/how-instagram-suggests-new-content/ How Instagram suggests new content], 2020-12-10<br /> # [https://instagram-engineering.com/five-things-i-learned-about-working-on-content-quality-at-instagram-5031b1342bea Five things I learned about working on content quality at Instagram], 2020-01-25<br /> # {{production}} [https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882 Instagram’s Explore Recommender System], 2019-11-26 [https://news.ycombinator.com/item?id=21638216 HackerNews discussion]<br /> #* 3-part funnel (2 layers of [[candidate generation]])<br /> #* domain-specific language (We have separation between model and filter config).<br /> #* account embeddings<br /> #* embedding-based<br /> #* “See fewer posts like this” – [[explicit feedback]]<br /> # [https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15 Offline Policy Evaluation: Run fewer, better A/B tests]<br /> # [https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/ DLRM: An advanced, open source deep learning recommendation model], 2019-07-02<br /> <br /> == Papers ==<br /> <br /> # {{ops}}{{performance}} [https://arxiv.org/pdf/2104.05158.pdf Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models], ISCA 2022 industry track<br /> # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]<br /> # {{ads}}{{neural}} [https://arxiv.org/pdf/2203.11014.pdf DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction], KDD 2022<br /> # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021<br /> # [https://research.fb.com/publications/preference-amplification-in-recommender-systems/ Preference Amplification in Recommender Systems], [[KDD 2021]]<br /> # {{search}} [https://www.kdd.org/kdd2020/accepted-papers/view/embedding-based-retrieval-in-facebook-search Embedding-based Retrieval in Facebook Search], [[KDD 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/compositional-embeddings-using-complementary-partitions-for-memory-efficien Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems], [[KDD 2020]]<br /> # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020<br /> # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)<br /> # {{ads}} [https://research.fb.com/publications/counterfactual-reasoning-and-learning-systems-the-example-of-computational-advertising/ Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising]<br /> # [https://arxiv.org/abs/1909.11810 Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems]<br /> # {{ads}} [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014<br /> # [https://cs.stanford.edu/~jure/pubs/linkpred-wsdm11.pdf Supervised Random Walks: Predicting and Recommending Links in Social Networks], [[WSDM 2011]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/facebookresearch/dlrm DLRM recommender]: click probability model<br /> # [https://github.com/facebook/prophet Prophet]: forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.<br /> # [https://github.com/facebook/FAI-PEP Facebook AI Performance Evaluation Platform]: framework and backend agnostic benchmarking platform to compare machine learning inferencing runtime metrics on a set of models and on variety of backends.<br /> # {{ltor}} [https://github.com/facebookresearch/StarSpace StarSpace]: Learning embeddings for classification, retrieval and ranking.<br /> # [https://github.com/facebookresearch/ReAgent ReAgent/Horizon]: end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. [https://reagent.ai/rasp_tutorial.html# Tutorial] contains e-commerce/recommendation example. [https://research.fb.com/publications/horizon-facebooks-open-source-applied-reinforcement-learning-platform/ paper]<br /> # [https://github.com/facebookresearch/faiss faiss]: library for efficient similarity search and clustering of dense vectors.<br /> # [https://github.com/facebookresearch/pysparnn pysparnn]: approximate nearest neighbor search for sparse data in Python.<br /> # [https://github.com/facebook/Ax Ax]: adaptive experimentation platform, [https://ax.dev/ ax.dev]<br /> <br /> == External links ==<br /> <br /> * [https://research.fb.com/blog/ research blog]<br /> * [https://research.fb.com/publications/ research publications]<br /> * [https://engineering.fb.com/ tech blog]<br /> * [https://instagram-engineering.com old Instagram tech blog]<br /> * GitHub repositories<br /> ** [https://github.com/facebook Facebook]<br /> ** [https://github.com/facebookresearch FB Research]<br /> ** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]<br /> ** [https://github.com/Instagram Instagram]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Meta&diff=2661 2023-05-19T12:08:07Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Meta''' is the company behind '''Facebook''', '''Instagram''', and '''WhatsApp'''.<br /> <br /> == Blog posts ==<br /> <br /> # [https://ai.facebook.com/blog/facebook-feed-improvements-ai-show-more-less/ The new AI-powered feature designed to improve Feed for everyone], 2022-10-05<br /> # [https://research.fb.com/blog/2021/08/when-do-recommender-systems-amplify-user-preferences-a-theoretical-framework-and-mitigation-strategies/ When do recommender systems amplify user preferences? A theoretical framework and mitigation strategies], 2021-08<br /> # [https://research.fb.com/efficient-tuning-of-online-systems-using-bayesian-optimization/ Efficient tuning of online systems using Bayesian optimization]<br /> # [https://engineering.fb.com/2020/12/17/ml-applications/diversified-recommendations/ On the value of diversified recommendations], 2020-12-17<br /> # [https://engineering.fb.com/2020/12/10/web/how-instagram-suggests-new-content/ How Instagram suggests new content], 2020-12-10<br /> # [https://instagram-engineering.com/five-things-i-learned-about-working-on-content-quality-at-instagram-5031b1342bea Five things I learned about working on content quality at Instagram], 2020-01-25<br /> # {{production}} [https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882 Instagram’s Explore Recommender System], 2019-11-26 [https://news.ycombinator.com/item?id=21638216 HackerNews discussion]<br /> #* 3-part funnel (2 layers of [[candidate generation]])<br /> #* domain-specific language (We have separation between model and filter config).<br /> #* account embeddings<br /> #* embedding-based<br /> #* “See fewer posts like this” – [[explicit feedback]]<br /> # [https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15 Offline Policy Evaluation: Run fewer, better A/B tests]<br /> # [https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/ DLRM: An advanced, open source deep learning recommendation model], 2019-07-02<br /> <br /> == Papers ==<br /> <br /> # {{ops}}{{performance}} [https://arxiv.org/pdf/2104.05158.pdf Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models], ISCA 2022 industry track<br /> # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]<br /> # [https://arxiv.org/pdf/2203.11014.pdf DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction], KDD 2022<br /> # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021<br /> # [https://research.fb.com/publications/preference-amplification-in-recommender-systems/ Preference Amplification in Recommender Systems], [[KDD 2021]]<br /> # {{search}} [https://www.kdd.org/kdd2020/accepted-papers/view/embedding-based-retrieval-in-facebook-search Embedding-based Retrieval in Facebook Search], [[KDD 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/compositional-embeddings-using-complementary-partitions-for-memory-efficien Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems], [[KDD 2020]]<br /> # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020<br /> # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)<br /> # {{ads}} [https://research.fb.com/publications/counterfactual-reasoning-and-learning-systems-the-example-of-computational-advertising/ Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising]<br /> # [https://arxiv.org/abs/1909.11810 Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems]<br /> # {{ads}} [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014<br /> # [https://cs.stanford.edu/~jure/pubs/linkpred-wsdm11.pdf Supervised Random Walks: Predicting and Recommending Links in Social Networks], [[WSDM 2011]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/facebookresearch/dlrm DLRM recommender]: click probability model<br /> # [https://github.com/facebook/prophet Prophet]: forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.<br /> # [https://github.com/facebook/FAI-PEP Facebook AI Performance Evaluation Platform]: framework and backend agnostic benchmarking platform to compare machine learning inferencing runtime metrics on a set of models and on variety of backends.<br /> # {{ltor}} [https://github.com/facebookresearch/StarSpace StarSpace]: Learning embeddings for classification, retrieval and ranking.<br /> # [https://github.com/facebookresearch/ReAgent ReAgent/Horizon]: end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. [https://reagent.ai/rasp_tutorial.html# Tutorial] contains e-commerce/recommendation example. [https://research.fb.com/publications/horizon-facebooks-open-source-applied-reinforcement-learning-platform/ paper]<br /> # [https://github.com/facebookresearch/faiss faiss]: library for efficient similarity search and clustering of dense vectors.<br /> # [https://github.com/facebookresearch/pysparnn pysparnn]: approximate nearest neighbor search for sparse data in Python.<br /> # [https://github.com/facebook/Ax Ax]: adaptive experimentation platform, [https://ax.dev/ ax.dev]<br /> <br /> == External links ==<br /> <br /> * [https://research.fb.com/blog/ research blog]<br /> * [https://research.fb.com/publications/ research publications]<br /> * [https://engineering.fb.com/ tech blog]<br /> * [https://instagram-engineering.com old Instagram tech blog]<br /> * GitHub repositories<br /> ** [https://github.com/facebook Facebook]<br /> ** [https://github.com/facebookresearch FB Research]<br /> ** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]<br /> ** [https://github.com/Instagram Instagram]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Alibaba&diff=2660 2023-05-15T17:01:12Z

<p>Zeno Gantner: /* Papers */</p> <hr /> <div>'''Alibaba''' is a Chinese e-commerce company.<br /> <br /> == Papers ==<br /> <br /> # {{search}} [https://arxiv.org/abs/1805.08524 Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search], [[IJCAI 2018]]<br /> # {{performance}} Wang et al.: [https://arxiv.org/pdf/1803.02349.pdf Billion-scale commodity embedding for e-commerce recommendation in Alibaba], [[KDD 2018]]<br /> # [https://arxiv.org/pdf/1801.02294.pdf Learning Tree-based Deep Model for Recommender Systems], KDD 2018<br /> # [https://arxiv.org/abs/1706.06978 Deep Interest Network for Click-Through Rate Prediction], KDD 2018<br /> # {{search}} {{ltor}} [https://dl.acm.org/doi/pdf/10.1145/3298689.3347000?download=true Personalized Re-ranking for Recommendation], [[RecSys 2019]]<br /> # {{neural}} [https://arxiv.org/abs/1809.03672 Deep Interest Evolution Network for Click-Through Rate Prediction], [[AAAI 2019]]<br /> # {{neural}} [https://arxiv.org/pdf/1905.06874.pdf Behavior Sequence Transformer for E-commerce Recommendation in Alibaba], [[DLP-KDD workshop 2019]]<br /> # {{neural}} Zhu et al.: Joint Optimization of Tree-based Index and Deep Model for Recommender Systems, [[NIPS 2019]]<br /> # {{neural}} [https://dl.acm.org/doi/abs/10.1145/3357384.3357895 BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer], [[CIKM 2019]]<br /> # [https://ieeexplore.ieee.org/abstract/document/9495161 Co-Displayed Items Aware List Recommendation], IEEE TODO, 2020<br /> # [https://dl.acm.org/doi/10.1145/3383313.3412238 PURS: Personalized Unexpected Recommender System for Improving User Satisfaction], [[RecSys 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/privileged-features-distillation-at-taobao-recommendations Privileged Features Distillation at Taobao Recommendations], [[KDD 2020]]<br /> # {{ltor}} [https://ieeexplore.ieee.org/abstract/document/9495161 AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online], IEEE TKDE 2021<br /> # {{ads}} [https://dl.acm.org/doi/abs/10.1145/3447548.3467086 Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling], [[KDD 2021]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/alibaba/EasyRec EasyRec]<br /> # [https://github.com/alibaba/DeepRec DeepRec]: uses TF 1.15<br /> <br /> == External links ==<br /> * [https://github.com/alibaba GitHub] (436 repos as of 2022-05, at least 2 of them relevant)<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2659 2023-05-09T07:38:51Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * &lt;s&gt;'''[[Alphabet]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Amazon]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Apple]]'''&lt;/s&gt;<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you]<br /> * [[Commendo]]<br /> * &lt;s&gt;'''[[Criteo]]'''&lt;/s&gt;<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] =&gt; [[Spotify]]<br /> * Etsy<br /> * &lt;s&gt;[[Facebook]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmaster]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmtipset]]&lt;/s&gt; (thanks Alan)<br /> * &lt;s&gt;[[Flixster]]&lt;/s&gt; (thanks srbecker)<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Google]] -&gt; [[Alphabet]]<br /> * &lt;s&gt;[[Gravity]]&lt;/s&gt;<br /> * &lt;s&gt;[[Hulu]]&lt;/s&gt;<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * &lt;s&gt;'''[[Instagram]]'''&lt;/s&gt;<br /> * [[Kaggle]]<br /> * &lt;s&gt;[[Knewton]]&lt;/s&gt;<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * &lt;s&gt;[[LinkedIn]]&lt;/s&gt;<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * &lt;s&gt;[[Moviepilot]]&lt;/s&gt; (thanks Alan)<br /> * [[Myrrix]]<br /> * '''[[Netflix]]'''<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * &lt;s&gt;[[Plista]]&lt;/s&gt;<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]] -- ACM RecSys<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * &lt;s&gt;[[Zalando]]&lt;/s&gt;<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2658 2023-05-08T12:26:46Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * &lt;s&gt;'''[[Alphabet]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Amazon]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Apple]]'''&lt;/s&gt;<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you]<br /> * [[Commendo]]<br /> * &lt;s&gt;'''[[Criteo]]'''&lt;/s&gt;<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] (ask Paul Lamere)<br /> * Etsy<br /> * &lt;s&gt;[[Facebook]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmaster]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmtipset]]&lt;/s&gt; (thanks Alan)<br /> * &lt;s&gt;[[Flixster]]&lt;/s&gt; (thanks srbecker)<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Google]] -&gt; [[Alphabet]]<br /> * [[Gracenote]] (ask Oscar)<br /> * &lt;s&gt;[[Gravity]]&lt;/s&gt;<br /> * &lt;s&gt;[[Hulu]]&lt;/s&gt;<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * &lt;s&gt;'''[[Instagram]]'''&lt;/s&gt;<br /> * [[Kaggle]]<br /> * &lt;s&gt;[[Knewton]]&lt;/s&gt;<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * &lt;s&gt;[[LinkedIn]]&lt;/s&gt;<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * &lt;s&gt;[[Moviepilot]]&lt;/s&gt; (thanks Alan)<br /> * [[Myrrix]]<br /> * '''[[Netflix]]'''<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * &lt;s&gt;[[Plista]]&lt;/s&gt;<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[Scarab Research]]<br /> * [[sematext]]<br /> * [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]<br /> * [[Shopify]]<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * &lt;s&gt;[[Zalando]]&lt;/s&gt;<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2657 2023-04-28T10:23:41Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * &lt;s&gt;'''[[Alphabet]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Amazon]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Apple]]'''&lt;/s&gt;<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you]<br /> * [[Commendo]]<br /> * &lt;s&gt;'''[[Criteo]]'''&lt;/s&gt;<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] (ask Paul Lamere)<br /> * Etsy<br /> * &lt;s&gt;[[Facebook]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmaster]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmtipset]]&lt;/s&gt; (thanks Alan)<br /> * &lt;s&gt;[[Flixster]]&lt;/s&gt; (thanks srbecker)<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Google]] -&gt; [[Alphabet]]<br /> * [[Gracenote]] (ask Oscar)<br /> * &lt;s&gt;[[Gravity]]&lt;/s&gt;<br /> * &lt;s&gt;[[Hulu]]&lt;/s&gt;<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * &lt;s&gt;'''[[Instagram]]'''&lt;/s&gt;<br /> * [[Kaggle]]<br /> * &lt;s&gt;[[Knewton]]&lt;/s&gt;<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * &lt;s&gt;[[LinkedIn]]&lt;/s&gt;<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * &lt;s&gt;[[Moviepilot]]&lt;/s&gt; (thanks Alan)<br /> * [[Myrrix]]<br /> * '''[[Netflix]]'''<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * &lt;s&gt;[[Plista]]&lt;/s&gt;<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[Scarab Research]]<br /> * [[sematext]]<br /> * [[Shopify]]<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * &lt;s&gt;[[Zalando]]&lt;/s&gt;<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2656 2023-04-28T09:34:29Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[aklamio]] [http://www.aklamio.com/] (ask Robert)<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * &lt;s&gt;'''[[Alphabet]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Amazon]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Apple]]'''&lt;/s&gt;<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]''' and [[TikTok]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you]<br /> * [[Commendo]]<br /> * '''[[Criteo]]'''<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] (ask Paul Lamere)<br /> * Etsy<br /> * &lt;s&gt;[[Facebook]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmaster]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmtipset]]&lt;/s&gt; (thanks Alan)<br /> * &lt;s&gt;[[Flixster]]&lt;/s&gt; (thanks srbecker)<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Google]] -&gt; [[Alphabet]]<br /> * [[Gracenote]] (ask Oscar)<br /> * &lt;s&gt;[[Gravity]]&lt;/s&gt;<br /> * &lt;s&gt;[[Hulu]]&lt;/s&gt;<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * &lt;s&gt;'''[[Instagram]]'''&lt;/s&gt;<br /> * [[Kaggle]]<br /> * &lt;s&gt;[[Knewton]]&lt;/s&gt;<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * &lt;s&gt;[[LinkedIn]]&lt;/s&gt;<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * &lt;s&gt;[[Moviepilot]]&lt;/s&gt; (thanks Alan)<br /> * [[Myrrix]]<br /> * '''[[Netflix]]'''<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * &lt;s&gt;[[Plista]]&lt;/s&gt;<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[Scarab Research]]<br /> * [[sematext]]<br /> * [[Shopify]]<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * &lt;s&gt;[[Zalando]]&lt;/s&gt;<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Criteo&diff=2655 2023-04-27T14:35:12Z

<p>Zeno Gantner: add paper Field-aware Factorization Machines in a Real-world Online Advertising System</p> <hr /> <div>'''Criteo''' is a French online advertisement company.<br /> <br /> == Software ==<br /> <br /> # [https://github.com/criteo-research/reco-gym reco-gym]<br /> <br /> == Papers ==<br /> <br /> # {{bandits}} [https://www.kdd.org/kdd2020/accepted-papers/view/blob-a-probabilistic-model-for-recommendation-that-combines-organic-and-ban BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals], [[KDD 2020]]<br /> # [https://dl.acm.org/doi/10.1145/3240323.3240360 Causal embeddings for recommendation], [[RecSys 2018]] (best long paper award)<br /> # {{ads}} [https://arxiv.org/pdf/1701.04099.pdf Field-aware Factorization Machines in a Real-world Online Advertising System], [[WWW 2017]]<br /> # {{ads}} Olivier Chapelle: [http://wnzhang.net/share/rtb-papers/ctr-bid.pdf Offline Evaluation of Response Prediction in Online Advertising Auctions], [[WWW 2015]]<br /> # {{experimentation}} [https://arxiv.org/pdf/1801.07030.pdf Offline A/B testing for Recommender Systems]<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=User:Zeno_Gantner&diff=2654 2023-04-27T14:31:07Z

<p>Zeno Gantner: /* Companies */</p> <hr /> <div>[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].<br /> Primary developer of the [[MyMediaLite]] recommender system library.<br /> Co-organizer of the [[Recommender Stammtisch]] in Berlin.<br /> <br /> [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]<br /> <br /> == TODO ==<br /> <br /> * page about Fashion RecSys workshop<br /> * add link to Google tutorial<br /> * add pages about PyTorch and TF recommendations<br /> * marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start<br /> * extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub<br /> * extend/create dataset template (link to downloads, Google scholar search, Papers with Code)<br /> * event/conference template (individual events and conference series)<br /> * create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.<br /> * page about [[Recsperts podcast]]<br /> <br /> == Article wishlist ==<br /> * [[A/B testing]]<br /> * &lt;s&gt;[[active learning]]&lt;/s&gt;<br /> * [[approximate nearest neighbor search]]<br /> * [[attribute-aware recommendation]]<br /> * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]<br /> * [[autoencoder]]<br /> * [[bag-of-items]]<br /> * [[bagging]]<br /> * [[bandit]] (-&gt; [[multi-arm bandit]])<br /> * [[beer recommendation]] -- very important task ... (ask Ben)<br /> * &lt;s&gt;[[blogs]]&lt;/s&gt;<br /> * [[BookCrossing]] (ask Cai-Nicolas)<br /> * [[capped binomial deviation]] ([[CBD]])<br /> * [[:Category:File format]]<br /> * [[CHI]] (ask Alan)<br /> * [[choice overload]] (ask Bart, Martijn, Dirk)<br /> * [[click stream]]<br /> * [[client-side recommendation]] (ask Chris)<br /> * [[code recommendation]] [http://t.co/QakdUh02]<br /> * [[CofiRank]] (ask Markus)<br /> * &lt;s&gt;[[cold-start problem]]&lt;/s&gt;<br /> * [[computational advertising]]<br /> * &lt;s&gt;[[content-based filtering]]&lt;/s&gt;<br /> * &lt;s&gt;[[context]]&lt;/s&gt;<br /> * &lt;s&gt;[[context-aware recommendation]]&lt;/s&gt;<br /> * [[contextual bandit]]<br /> * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]<br /> * [[data analytics]]<br /> * [[data mining]]<br /> * [[decision theory]]<br /> * [[deep learning]]<br /> * [[distance]]<br /> * [[distributed computing]] (ask Sebastian)<br /> * [[distributed matrix factorization]] (ask Rainer)<br /> * [[Eigentaste]]<br /> * [[Epinions dataset]]<br /> * [[Explanations]] (ask Nava)<br /> * [[exploration vs. exploitation]]<br /> * [[evaluation]]<br /> * [[factorization model]], [[factorization models]]<br /> * [[FAQ for recommender system developers]]<br /> * [[FAQ for recommender system users]]<br /> * [[Fashion recommendation]], [[Fashion recommendations]]<br /> * [[Filter bubble]] (ask Alan and Neal)<br /> * [[Flixster dataset]]<br /> * [[F measure]], [[F1 measure]]<br /> * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]<br /> * [[GraphChi]] (ask Danny)<br /> * [[GraphLab]] (ask Danny)<br /> * [[Greg Linden]]<br /> * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]<br /> * &lt;s&gt;[[group recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[Harry Potter effect]]&lt;/s&gt;<br /> * [[HCI]]<br /> * [[higher-order SVD]] (ask Steffen)<br /> * &lt;s&gt;[[hybrid recommendation]]&lt;/s&gt;<br /> * [[hyperparameter]]<br /> * [[incentive]]<br /> * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]<br /> * [[information retrieval]]<br /> * [[Introduction to recommender systems]]<br /> * [[Introduction to recommender system algorithms]]<br /> * [[IPTV]] (ask Chris)<br /> * [[item]]<br /> * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]<br /> * [[Jaccard index]]<br /> * [[Jester]]<br /> * [[job recommendation]]<br /> * [[Joke recommendation]]<br /> * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]<br /> * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]<br /> * [[keyword-based recommendation]]<br /> * &lt;s&gt;[[kNN]]&lt;/s&gt;<br /> * [[lab testing]]<br /> * [[latency]] (ask Sebastian)<br /> * [[latent factor model]]<br /> * [[learning]]<br /> * [[learning to rank]]<br /> * [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms<br /> * [[List of recommender system meetings]]<br /> * [[live evaluation]] (ask Andreas H./Alan)<br /> * [[location-aware recommendation]]<br /> * [[London RecSys Meetup]] (ask Neal)<br /> * [[long tail]] (ask Oscar)<br /> * [[machine learning]]<br /> * [[Markov chain]] (ask Christoph)<br /> * [[Markov decision process]], [[MDP]]<br /> * [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)<br /> * &lt;s&gt;[[matrix factorization]]&lt;/s&gt;<br /> * [[maximum a-priori estimation]] ([[MAP]])<br /> * [[maximum inner product search]]<br /> * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]<br /> * [[mean reciprocal rank]]<br /> * &lt;s&gt;[[Million Song Dataset]]&lt;/s&gt;<br /> * &lt;s&gt;[[Million Song Dataset Challenge]]&lt;/s&gt; (&lt;s&gt;ask Brian McFee&lt;/s&gt;)<br /> * [[MinHash]]<br /> * [[MLOps]<br /> * [[model]]<br /> * [[monetization]]<br /> * [[Movie Hack Day]] (ask Jannis and Alan)<br /> * [[multi-arm bandit]] (ask Matt)<br /> * [[Music Hack Day]] (ask Amelie)<br /> * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)<br /> * &lt;s&gt;[[music recommendation]]&lt;/s&gt;<br /> * &lt;s&gt;[[MyMedia]]&lt;/s&gt; (thank you Alan!)<br /> * &lt;s&gt;[[NDCG]]&lt;/s&gt;<br /> * [[neural networks]]<br /> * [[news recommendation]]<br /> * [[offline experiment]]<br /> * [[one-class feedback]]<br /> * [[overfitting]]<br /> * [[page composition]]<br /> * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)<br /> * [[Papers with Code]]<br /> * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)<br /> * [[parallel matrix factorization]]<br /> * [[parameter]]<br /> * &lt;s&gt;[[Pearson correlation]]&lt;/s&gt;<br /> * [[personalization]]<br /> * [[personalized advertising]]<br /> * [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]<br /> * [[personalized search]]<br /> * [[positive-only feedback]]<br /> * [[preference elicitation]] (ask Martijn and Bart)<br /> * [[product recommendation]]<br /> * [[public transport]] (ask Neal)<br /> * [[R]]<br /> * [[ranking]]<br /> * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)<br /> * [[recipe recommendation]]<br /> * [[recommendation of financial products]]<br /> * [[recommender lab]] (ask Michael H.)<br /> * &lt;s&gt;[[recommender system]]&lt;/s&gt;<br /> * [[RecSys meetups]] (do it yourself)<br /> * [[reinforcement learning]] (ask Tobias)<br /> * &lt;s&gt;[[regularization]]&lt;/s&gt;<br /> * [[reputation]]<br /> * [[restricted Boltzmann machine]] (ask Andriy)<br /> * [[review]]<br /> * [[Ringo]]<br /> * [[scalability]] (ask Sebastian)<br /> * [[semi-supervised learning]]<br /> * [[sequential recommendation]]<br /> * [[serendipity]] (ask Alan, ask Ben)<br /> * [[session-based recommendation]]<br /> * [[similarity]]<br /> * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]<br /> * [[software as a service]] (ask Manuel B.)<br /> * [[software recommendation]]<br /> * [[standard benchmarks]] TODO<br /> * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art<br /> * [[stream processing]]<br /> * &lt;s&gt;[[SVD]]&lt;/s&gt;<br /> * &lt;s&gt;[[SVD++]], [[SVDPlusPlus]]&lt;/s&gt;<br /> * [[TaFeng]]<br /> * &lt;s&gt;[[tag]]&lt;/s&gt; (thanks Alan)<br /> * [[Tanimoto coefficient]] --&gt; [[Jaccard index]]<br /> * [[Tapestry]]<br /> * [[tensor factorization]] (ask Steffen)<br /> * [[text-based recommendation]]<br /> * [[text mining]]<br /> * [[time-aware recommendation]]<br /> * [[transductive learning]]<br /> * [[Tucker decomposition]] (ask Steffen)<br /> * [[TV program recommendation]] (ask Chris)<br /> * [[UMAP]]<br /> * [[user]]<br /> * [[user-item matrix]]<br /> * [[user model]]<br /> * [[user preferences]]<br /> * [[user recommendation]]<br /> * [[user satisfaction]]<br /> * [[video recommendation]]<br /> * [[WSDM]]<br /> * [[Yahoo Movie Dataset]]<br /> <br /> === RecSys people ===<br /> <br /> * [[Joseph Konstan]]<br /> * [[John Riedl]]<br /> * [[Yehuda Koren]]<br /> * [[Pearl Pu]]<br /> * [[Greg Linden]]<br /> * [[Paul Lamere]]<br /> * [[Ted Dunning]]<br /> * [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&amp;hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs<br /> * [[Ralf Herbrich]]<br /> <br /> === Companies ===<br /> * [[aklamio]] [http://www.aklamio.com/] (ask Robert)<br /> * [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]<br /> * &lt;s&gt;'''[[Alphabet]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Amazon]]'''&lt;/s&gt;<br /> * &lt;s&gt;'''[[Apple]]'''&lt;/s&gt;<br /> * [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]<br /> * [[BMAT]]<br /> * [[Bol.com]]<br /> * '''[[ByteDance]]'''<br /> * [[Commendo]]<br /> * '''[[Criteo]]'''<br /> * [[Directed Edge]] -- http://www.directededge.com<br /> * [[EBay]]<br /> * [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] (ask Paul Lamere)<br /> * Etsy<br /> * &lt;s&gt;[[Facebook]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmaster]]&lt;/s&gt;<br /> * &lt;s&gt;[[Filmtipset]]&lt;/s&gt; (thanks Alan)<br /> * &lt;s&gt;[[Flixster]]&lt;/s&gt; (thanks srbecker)<br /> * [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]<br /> * [[Froomle]]<br /> * [[Google]] -&gt; [[Alphabet]]<br /> * [[Gracenote]] (ask Oscar)<br /> * &lt;s&gt;[[Gravity]]&lt;/s&gt;<br /> * &lt;s&gt;[[Hulu]]&lt;/s&gt;<br /> * [[Hunch]]<br /> * [[Ikea]]<br /> * &lt;s&gt;'''[[Instagram]]'''&lt;/s&gt;<br /> * [[Kaggle]]<br /> * &lt;s&gt;[[Knewton]]&lt;/s&gt;<br /> * [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]<br /> * &lt;s&gt;[[LinkedIn]]&lt;/s&gt;<br /> * [[Lumi]]<br /> * '''[[Meta]]'''<br /> * [[Microsoft]]<br /> * &lt;s&gt;[[Moviepilot]]&lt;/s&gt; (thanks Alan)<br /> * [[Myrrix]]<br /> * '''[[Netflix]]'''<br /> * [[Nokia]] -- add 2011 Buzzwords presentation<br /> * [[Otto]]<br /> * [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]<br /> * [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)<br /> * &lt;s&gt;[[Plista]]&lt;/s&gt;<br /> * [[Prudsys]]<br /> * [[Recommind]] [http://www.recommind.com/]<br /> * [[RichRelevance]] (ask Darren)<br /> * [[Samsung]]<br /> * [[Scarab Research]]<br /> * [[sematext]]<br /> * [[Shopify]]<br /> * [[Sidebar]]<br /> * [[SoundCloud]]<br /> * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625]<br /> * [[Strands]]<br /> * [[TiVo]]<br /> * [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html]<br /> * '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]<br /> * [[YooChoose]]<br /> * &lt;s&gt;[[Zalando]]&lt;/s&gt;<br /> * [[Zite]]<br /> <br /> == RecSys slides, classes, etc. ==<br /> * http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&amp;cache=cache&amp;media=fatoracao_matrizes.pdf<br /> * [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)<br /> * http://alex.smola.org/teaching/berkeley2012/recommender.html<br /> * http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Meta&diff=2653 2023-04-26T16:19:35Z

<p>Zeno Gantner: add paper ''Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models'', ISCA 2022 industry track</p> <hr /> <div>'''Meta''' is the company behind '''Facebook''', '''Instagram''', and '''WhatsApp'''.<br /> <br /> == Blog posts ==<br /> <br /> # [https://ai.facebook.com/blog/facebook-feed-improvements-ai-show-more-less/ The new AI-powered feature designed to improve Feed for everyone], 2022-10-05<br /> # [https://research.fb.com/blog/2021/08/when-do-recommender-systems-amplify-user-preferences-a-theoretical-framework-and-mitigation-strategies/ When do recommender systems amplify user preferences? A theoretical framework and mitigation strategies], 2021-08<br /> # [https://research.fb.com/efficient-tuning-of-online-systems-using-bayesian-optimization/ Efficient tuning of online systems using Bayesian optimization]<br /> # [https://engineering.fb.com/2020/12/17/ml-applications/diversified-recommendations/ On the value of diversified recommendations], 2020-12-17<br /> # [https://engineering.fb.com/2020/12/10/web/how-instagram-suggests-new-content/ How Instagram suggests new content], 2020-12-10<br /> # [https://instagram-engineering.com/five-things-i-learned-about-working-on-content-quality-at-instagram-5031b1342bea Five things I learned about working on content quality at Instagram], 2020-01-25<br /> # {{production}} [https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882 Instagram’s Explore Recommender System], 2019-11-26 [https://news.ycombinator.com/item?id=21638216 HackerNews discussion]<br /> #* 3-part funnel (2 layers of [[candidate generation]])<br /> #* domain-specific language (We have separation between model and filter config).<br /> #* account embeddings<br /> #* embedding-based<br /> #* “See fewer posts like this” – [[explicit feedback]]<br /> # [https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15 Offline Policy Evaluation: Run fewer, better A/B tests]<br /> # [https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/ DLRM: An advanced, open source deep learning recommendation model], 2019-07-02<br /> <br /> == Papers ==<br /> <br /> # {{ops}}{{performance}} [https://arxiv.org/pdf/2104.05158.pdf Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models], ISCA 2022 industry track<br /> # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]<br /> # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021<br /> # [https://research.fb.com/publications/preference-amplification-in-recommender-systems/ Preference Amplification in Recommender Systems], [[KDD 2021]]<br /> # {{search}} [https://www.kdd.org/kdd2020/accepted-papers/view/embedding-based-retrieval-in-facebook-search Embedding-based Retrieval in Facebook Search], [[KDD 2020]]<br /> # [https://www.kdd.org/kdd2020/accepted-papers/view/compositional-embeddings-using-complementary-partitions-for-memory-efficien Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems], [[KDD 2020]]<br /> # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020<br /> # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)<br /> # {{ads}} [https://research.fb.com/publications/counterfactual-reasoning-and-learning-systems-the-example-of-computational-advertising/ Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising]<br /> # [https://arxiv.org/abs/1909.11810 Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems]<br /> # {{ads}} [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014<br /> # [https://cs.stanford.edu/~jure/pubs/linkpred-wsdm11.pdf Supervised Random Walks: Predicting and Recommending Links in Social Networks], [[WSDM 2011]]<br /> <br /> == Software ==<br /> <br /> # [https://github.com/facebookresearch/dlrm DLRM recommender]: click probability model<br /> # [https://github.com/facebook/prophet Prophet]: forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.<br /> # [https://github.com/facebook/FAI-PEP Facebook AI Performance Evaluation Platform]: framework and backend agnostic benchmarking platform to compare machine learning inferencing runtime metrics on a set of models and on variety of backends.<br /> # {{ltor}} [https://github.com/facebookresearch/StarSpace StarSpace]: Learning embeddings for classification, retrieval and ranking.<br /> # [https://github.com/facebookresearch/ReAgent ReAgent/Horizon]: end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. [https://reagent.ai/rasp_tutorial.html# Tutorial] contains e-commerce/recommendation example. [https://research.fb.com/publications/horizon-facebooks-open-source-applied-reinforcement-learning-platform/ paper]<br /> # [https://github.com/facebookresearch/faiss faiss]: library for efficient similarity search and clustering of dense vectors.<br /> # [https://github.com/facebookresearch/pysparnn pysparnn]: approximate nearest neighbor search for sparse data in Python.<br /> # [https://github.com/facebook/Ax Ax]: adaptive experimentation platform, [https://ax.dev/ ax.dev]<br /> <br /> == External links ==<br /> <br /> * [https://research.fb.com/blog/ research blog]<br /> * [https://research.fb.com/publications/ research publications]<br /> * [https://engineering.fb.com/ tech blog]<br /> * [https://instagram-engineering.com old Instagram tech blog]<br /> * GitHub repositories<br /> ** [https://github.com/facebook Facebook]<br /> ** [https://github.com/facebookresearch FB Research]<br /> ** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]<br /> ** [https://github.com/Instagram Instagram]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Main_Page&diff=2652 2023-03-13T16:51:11Z

<p>Zeno Gantner: </p> <hr /> <div>{{DISPLAYTITLE:&lt;span style=&quot;display:none&quot;&gt;{{FULLPAGENAME}}&lt;/span&gt;}}<br /> &lt;div id=&quot;mainpage&quot;&gt;<br /> &lt;/div&gt;<br /> __NOTOC__<br /> &lt;!-- Beginning of header section --&gt;<br /> {|style=&quot;width:100%;margin-top:+.9em;background-color:#fcfcfc;border:1px solid #ccc&quot;<br /> |style=&quot;width:56%;color:#000&quot;|<br /> {|style=&quot;width:100%;border:solid 0px;background:none&quot;<br /> |-<br /> |style=&quot;width:100%;text-align:center;white-space:nowrap;color:#000&quot; |<br /> &lt;div style=&quot;font-size:162%;border:none;margin: 0;padding:.1em;color:#000&quot;&gt;Welcome to the Recommender Systems Wiki (RecSysWiki) &lt;/div&gt;<br /> &lt;div style=&quot;top:+0.2em;font-size: 95%&quot;&gt;''sharing information on all aspects of [[Recommender System|Recommender Systems]]''&lt;/div&gt;<br /> &lt;div id=&quot;articlecount&quot; style=&quot;width:100%;text-align:center;font-size:85%;&quot;&gt;'''{{NUMBEROFPAGES}}''' pages and '''{{NUMBEROFARTICLES}}''' articles, created by [[Special:ActiveUsers|{{NUMBEROFUSERS}} users]] as of {{CURRENTDAYNAME}}, {{CURRENTMONTHNAME}} {{CURRENTDAY}}, {{CURRENTYEAR}}&lt;/div&gt;<br /> &lt;div style=&quot;width:100%;text-align:center;font-size:85%;&quot;&gt;started on February 10th, 2011&lt;/div&gt;<br /> |}<br /> |}<br /> <br /> [[RecSysWiki:About|This Wiki]] is about everything related to [[Recommender System|Recommender Systems]].<br /> <br /> [[Special:CreateAccount|Registration]] is open to anyone who wishes to contribute, but please contact an [[User:IgorBrigadir|administrator]] to get a new account.<br /> <br /> &lt;!-- End of header section / beginning of left-column --&gt;<br /> {|style=&quot;border-spacing:8px;margin:0px -8px&quot;<br /> |class=&quot;MainPageBG&quot; style=&quot;width:55%;border:1px solid #cef2e0;background-color:#f5fffa;vertical-align:top;color:#000&quot;|<br /> {|width=&quot;100%&quot; cellpadding=&quot;2&quot; cellspacing=&quot;5&quot; style=&quot;vertical-align:top;background-color:#f5fffa&quot;<br /> ! &lt;h2 style=&quot;margin:0;background-color:#cef2e0;font-size:120%;font-weight:bold;border:1px solid #a3bfb1;text-align:left;color:#000;padding:0.2em 0.4em;&quot;&gt;Categories in RecSysWiki&lt;/h2&gt;<br /> |-<br /> |style=&quot;color:#000&quot;|&lt;!-- Please keep this list in alphabetical order --&gt;<br /> * [[:Category:API|APIs]]<br /> * [[:Category:Book|Books]]<br /> * [[:Category:Class|Classes]]<br /> * [[:Category:Company|Companies]]<br /> * [[:Category:Competition|Competitions]]<br /> * [[:Category:Conference|Conferences]]<br /> * [[:Category:Dataset|Datasets]]<br /> * [[:Category:Educational recommender systems|Educational Recommender Systems]]<br /> * [[:Category:Evaluation|Evaluation]]<br /> * [[:Category:Evaluation measure|Evaluation Measures]]<br /> * [[:Category:Event|Events]]<br /> * [[:Category:Fashion recommendation|Fashion Recommendation]]<br /> * [[:Category:Job offer|Job Offers]]<br /> * [[:Category:Journal|Journals]]<br /> * [[:Category:List|Lists]]<br /> * [[:Category:Literature|Literature]]<br /> * [[:Category:Method|Methods]]<br /> * [[:Category:Movie recommendation|Movie Recommendation]]<br /> * [[:Category:Music recommendation|Music Recommendation]]<br /> * [[:Category:Paper|Papers]]<br /> * [[:Category:People|People]]<br /> * [[:Category:Project|Projects]]<br /> * [[:Category:Research group|Research Groups]]<br /> * [[:Category:Software|Software]]<br /> * [[:Category:Task|Task]]<br /> * [[:Category:User-centric evaluation|User-centric evaluation]]<br /> * [[:Category:Workshop|Workshops]]<br /> <br /> |-<br /> |}&lt;!-- Start of right-column --&gt;<br /> |class=&quot;MainPageBG&quot; style=&quot;width:65%;border:1px solid #cedff2;background-color:#f5faff;vertical-align:top&quot;|<br /> {| width=&quot;100%&quot; cellpadding=&quot;2&quot; cellspacing=&quot;5&quot; style=&quot;vertical-align:top;background-color:#f5faff&quot;<br /> !<br /> &lt;h2 style=&quot;margin:0;background-color:#cedff2;font-size:120%;font-weight:bold;border:1px solid #a3b0bf;text-align:left;color:#000;padding:0.2em 0.4em;&quot;&gt;Information on RecSysWiki&lt;/h2&gt;<br /> |-<br /> |style=&quot;color:#000&quot;|&lt;!-- Please keep this list in alphabetical order --&gt;<br /> * [[:RecSysWiki:Current events| Current Events]]<br /> * [[Special:Statistics|RecSysWiki statistics]]<br /> * [[Special:AllPages|All pages]]<br /> * [[Special:Categories|All categories]]<br /> * [[Special:WantedPages|Wanted pages]]<br /> * [[To Do List]] - ''please help by contributing''<br /> <br /> &lt;h2 style=&quot;margin:0;background-color:#cedff2;font-size:120%;font-weight:bold;border:1px solid #a3b0bf;text-align:left;color:#000;padding:0.2em 0.4em;&quot;&gt;Recent additions and updates&lt;/h2&gt;<br /> |-<br /> |style=&quot;color:#000&quot;|&lt;!-- Please keep this list ordered by date --&gt;<br /> &lt;!-- How long should this list be? It should match the size of the leftmost column. --&gt;<br /> * [[NVidia]] &lt;!-- 2023-03-13 --&gt;<br /> * [[Spotify]] &lt;!-- 2023-02-24 --&gt;<br /> * [[ZDF]] &lt;!-- 2022-12-15 --&gt;<br /> * [[Meta]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Alphabet]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Criteo]] &lt;!-- 2022-12-03 --&gt;<br /> * [[ASOS]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Apple]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Alibaba]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Amazon]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Zalando]] &lt;!-- 2022-12-01 --&gt;<br /> * [[Recommendation Datasets]] &lt;!-- 2015-08-06 --&gt;<br /> * [[Recommendation Software]] &lt;!-- 2015-08-05 --&gt;<br /> * [[List of recommender systems master's theses|Master's Theses]] &lt;!-- 2014-09-20 --&gt;<br /> * [[TagRec]] &lt;!-- 2015-04-09 --&gt;<br /> * [[CrowdRec]] &lt;!-- 2014-09-20 --&gt;<br /> * [[RecSysChallenge]] &lt;!-- 2014-09-20 --&gt;<br /> * [[LibRec]] &lt;!-- 2014-09-20 --&gt;<br /> |-<br /> !<br /> |-<br /> |}<br /> <br /> |-<br /> |}<br /> <br /> <br /> <br /> [[Category:RecSys Wiki Information]]<br /> &lt;!-- {{#TwitterFBLike:}} --&gt;</div>

Zeno Gantner https://recsyswiki.com/index.php?title=NVidia&diff=2651 2023-03-13T16:50:35Z

<p>Zeno Gantner: Created page with &quot;== Blog posts == * [https://medium.com/nvidia-merlin/building-ranking-models-powered-by-multi-task-learning-with-merlin-and-tensorflow-4b4f993f7cc3 Building ranking models pow...&quot;</p> <hr /> <div>== Blog posts ==<br /> * [https://medium.com/nvidia-merlin/building-ranking-models-powered-by-multi-task-learning-with-merlin-and-tensorflow-4b4f993f7cc3 Building ranking models powered by multi-task learning with Merlin and TensorFlow], 2023-03-13<br /> <br /> [[Category:Company]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Spotify&diff=2650 2023-03-10T15:16:29Z

<p>Zeno Gantner: Survival Analysis Meets Reinforcement Learning</p> <hr /> <div>'''Spotify''' is an audio streaming service.<br /> <br /> == Papers ==<br /> ...<br /> <br /> == Blog posts ==<br /> * [https://newsroom.spotify.com/2023-02-22/spotify-debuts-a-new-ai-dj-right-in-your-pocket/ Spotify Debuts a New AI DJ, Right in Your Pocket], 2023-02-22<br /> * [https://research.atspotify.com/2023/02/users-interests-are-multi-faceted-recommendation-models-should-be-too/ Users’ interests are multi-faceted: recommendation models should be too], 2023-02-22<br /> * [https://research.atspotify.com/2022/11/survival-analysis-meets-reinforcement-learning/ Survival Analysis Meets Reinforcement Learning], 2022-11-25<br /> <br /> == External links ==<br /> * https://research.atspotify.com/<br /> <br /> <br /> [[Category:Company]]<br /> [[Category:Music recommendation]]</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Main_Page&diff=2649 2023-02-24T10:46:40Z

<p>Zeno Gantner: +Spotify</p> <hr /> <div>{{DISPLAYTITLE:&lt;span style=&quot;display:none&quot;&gt;{{FULLPAGENAME}}&lt;/span&gt;}}<br /> &lt;div id=&quot;mainpage&quot;&gt;<br /> &lt;/div&gt;<br /> __NOTOC__<br /> &lt;!-- Beginning of header section --&gt;<br /> {|style=&quot;width:100%;margin-top:+.9em;background-color:#fcfcfc;border:1px solid #ccc&quot;<br /> |style=&quot;width:56%;color:#000&quot;|<br /> {|style=&quot;width:100%;border:solid 0px;background:none&quot;<br /> |-<br /> |style=&quot;width:100%;text-align:center;white-space:nowrap;color:#000&quot; |<br /> &lt;div style=&quot;font-size:162%;border:none;margin: 0;padding:.1em;color:#000&quot;&gt;Welcome to the Recommender Systems Wiki (RecSysWiki) &lt;/div&gt;<br /> &lt;div style=&quot;top:+0.2em;font-size: 95%&quot;&gt;''sharing information on all aspects of [[Recommender System|Recommender Systems]]''&lt;/div&gt;<br /> &lt;div id=&quot;articlecount&quot; style=&quot;width:100%;text-align:center;font-size:85%;&quot;&gt;'''{{NUMBEROFPAGES}}''' pages and '''{{NUMBEROFARTICLES}}''' articles, created by [[Special:ActiveUsers|{{NUMBEROFUSERS}} users]] as of {{CURRENTDAYNAME}}, {{CURRENTMONTHNAME}} {{CURRENTDAY}}, {{CURRENTYEAR}}&lt;/div&gt;<br /> &lt;div style=&quot;width:100%;text-align:center;font-size:85%;&quot;&gt;started on February 10th, 2011&lt;/div&gt;<br /> |}<br /> |}<br /> <br /> [[RecSysWiki:About|This Wiki]] is about everything related to [[Recommender System|Recommender Systems]].<br /> <br /> [[Special:CreateAccount|Registration]] is open to anyone who wishes to contribute, but please contact an [[User:IgorBrigadir|administrator]] to get a new account.<br /> <br /> &lt;!-- End of header section / beginning of left-column --&gt;<br /> {|style=&quot;border-spacing:8px;margin:0px -8px&quot;<br /> |class=&quot;MainPageBG&quot; style=&quot;width:55%;border:1px solid #cef2e0;background-color:#f5fffa;vertical-align:top;color:#000&quot;|<br /> {|width=&quot;100%&quot; cellpadding=&quot;2&quot; cellspacing=&quot;5&quot; style=&quot;vertical-align:top;background-color:#f5fffa&quot;<br /> ! &lt;h2 style=&quot;margin:0;background-color:#cef2e0;font-size:120%;font-weight:bold;border:1px solid #a3bfb1;text-align:left;color:#000;padding:0.2em 0.4em;&quot;&gt;Categories in RecSysWiki&lt;/h2&gt;<br /> |-<br /> |style=&quot;color:#000&quot;|&lt;!-- Please keep this list in alphabetical order --&gt;<br /> * [[:Category:API|APIs]]<br /> * [[:Category:Book|Books]]<br /> * [[:Category:Class|Classes]]<br /> * [[:Category:Company|Companies]]<br /> * [[:Category:Competition|Competitions]]<br /> * [[:Category:Conference|Conferences]]<br /> * [[:Category:Dataset|Datasets]]<br /> * [[:Category:Educational recommender systems|Educational Recommender Systems]]<br /> * [[:Category:Evaluation|Evaluation]]<br /> * [[:Category:Evaluation measure|Evaluation Measures]]<br /> * [[:Category:Event|Events]]<br /> * [[:Category:Fashion recommendation|Fashion Recommendation]]<br /> * [[:Category:Job offer|Job Offers]]<br /> * [[:Category:Journal|Journals]]<br /> * [[:Category:List|Lists]]<br /> * [[:Category:Literature|Literature]]<br /> * [[:Category:Method|Methods]]<br /> * [[:Category:Movie recommendation|Movie Recommendation]]<br /> * [[:Category:Music recommendation|Music Recommendation]]<br /> * [[:Category:Paper|Papers]]<br /> * [[:Category:People|People]]<br /> * [[:Category:Project|Projects]]<br /> * [[:Category:Research group|Research Groups]]<br /> * [[:Category:Software|Software]]<br /> * [[:Category:Task|Task]]<br /> * [[:Category:User-centric evaluation|User-centric evaluation]]<br /> * [[:Category:Workshop|Workshops]]<br /> <br /> |-<br /> |}&lt;!-- Start of right-column --&gt;<br /> |class=&quot;MainPageBG&quot; style=&quot;width:65%;border:1px solid #cedff2;background-color:#f5faff;vertical-align:top&quot;|<br /> {| width=&quot;100%&quot; cellpadding=&quot;2&quot; cellspacing=&quot;5&quot; style=&quot;vertical-align:top;background-color:#f5faff&quot;<br /> !<br /> &lt;h2 style=&quot;margin:0;background-color:#cedff2;font-size:120%;font-weight:bold;border:1px solid #a3b0bf;text-align:left;color:#000;padding:0.2em 0.4em;&quot;&gt;Information on RecSysWiki&lt;/h2&gt;<br /> |-<br /> |style=&quot;color:#000&quot;|&lt;!-- Please keep this list in alphabetical order --&gt;<br /> * [[:RecSysWiki:Current events| Current Events]]<br /> * [[Special:Statistics|RecSysWiki statistics]]<br /> * [[Special:AllPages|All pages]]<br /> * [[Special:Categories|All categories]]<br /> * [[Special:WantedPages|Wanted pages]]<br /> * [[To Do List]] - ''please help by contributing''<br /> <br /> &lt;h2 style=&quot;margin:0;background-color:#cedff2;font-size:120%;font-weight:bold;border:1px solid #a3b0bf;text-align:left;color:#000;padding:0.2em 0.4em;&quot;&gt;Recent additions and updates&lt;/h2&gt;<br /> |-<br /> |style=&quot;color:#000&quot;|&lt;!-- Please keep this list ordered by date --&gt;<br /> &lt;!-- How long should this list be? It should match the size of the leftmost column. --&gt;<br /> * [[Spotify]] &lt;!-- 2023-02-24 --&gt;<br /> * [[ZDF]] &lt;!-- 2022-12-15 --&gt;<br /> * [[Meta]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Alphabet]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Criteo]] &lt;!-- 2022-12-03 --&gt;<br /> * [[ASOS]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Apple]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Alibaba]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Amazon]] &lt;!-- 2022-12-03 --&gt;<br /> * [[Zalando]] &lt;!-- 2022-12-01 --&gt;<br /> * [[Recommendation Datasets]] &lt;!-- 2015-08-06 --&gt;<br /> * [[Recommendation Software]] &lt;!-- 2015-08-05 --&gt;<br /> * [[List of recommender systems master's theses|Master's Theses]] &lt;!-- 2014-09-20 --&gt;<br /> * [[TagRec]] &lt;!-- 2015-04-09 --&gt;<br /> * [[CrowdRec]] &lt;!-- 2014-09-20 --&gt;<br /> * [[RecSysChallenge]] &lt;!-- 2014-09-20 --&gt;<br /> * [[LibRec]] &lt;!-- 2014-09-20 --&gt;<br /> * [[Test.fm]] &lt;!-- 2014-09-20 --&gt;<br /> |-<br /> !<br /> |-<br /> |}<br /> <br /> |-<br /> |}<br /> <br /> <br /> <br /> [[Category:RecSys Wiki Information]]<br /> &lt;!-- {{#TwitterFBLike:}} --&gt;</div>

Zeno Gantner https://recsyswiki.com/index.php?title=Spotify&diff=2648 2023-02-24T10:45:43Z

<p>Zeno Gantner: Category:Music recommendation</p> <hr /> <div>'''Spotify''' is an audio streaming service.<br /> <br /> == Papers ==<br /> ...<br /> <br /> == Blog posts ==<br /> * [https://newsroom.spotify.com/2023-02-22/spotify-debuts-a-new-ai-dj-right-in-your-pocket/ Spotify Debuts a New AI DJ, Right in Your Pocket], 2023-02-22<br /> * [https://research.atspotify.com/2023/02/users-interests-are-multi-faceted-recommendation-models-should-be-too/ Users’ interests are multi-faceted: recommendation models should be too], 2023-02-22<br /> <br /> == External links ==<br /> * https://research.atspotify.com/<br /> <br /> <br /> [[Category:Company]]<br /> [[Category:Music recommendation]]</div>

Zeno Gantner