Alphabet

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Alphabet is the parent company of Google and many of its (former) subsidiaries, for example YouTube and DeepMind.


Tutorials

  1. Rules of Machine Learning
  2. 4-hour tutorial on Recommendation Systems

Papers

  1. πŸ”© Data Management Principles, book chapter in Reliable Machine Learning: Applying SRE Principles to ML in Production, 2022
  2. 🧠 Scale Calibration of Deep Ranking Models, KDD 2022
  3. Bootstrapping Recommendations at Chrome Web Store, KDD 2021
  4. πŸ”© β€œEveryone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI, CHI 2021
  5. πŸ“‘πŸ§  Are Neural Rankers Still Outperformed By Gradient Boosted Decision Trees?, ICLR 2021
  6. πŸ”‘ Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations, RecSys 2021
  7. 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
  8. Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems
  9. 🎰 [Bandits Revisited], NeurIPS 2020
  10. Rankmax: An Adaptive Projection Alternative to the Softmax Function, NeurIPS 2020
  11. πŸƒ MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search, AISTATS 2020
  12. πŸ“‘ Interpretable Learning-to-Rank with Generalized Additive Models, 2020
  13. Off-policy Learning in Two-stage Recommender Systems, WWW 2020
  14. 🧠 Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations, WWW 2020; Google Play
  15. πŸ€” Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies, KDD 2020
  16. 🧠 Neural Collaborative Filtering vs. Matrix Factorization Revisited, RecSys 2020
  17. Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval, CIKM 2020
  18. πŸ“‘ DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems, 2020
  19. Template:Runtime Accelerating Large-Scale Inference with Anisotropic Vector Quantization, 2019
  20. Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology, 2019
  21. Seq2Slate: Re-ranking and Slate Optimization with RNNs, 2019
  22. 🧠 Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks, SIGIR 2019 (short paper)
  23. 🧠Towards Neural Mixture Recommender for Long Range Dependent User Sequences, WWW 2019
  24. Top-K Off-Policy Correction for a REINFORCE Recommender System, WSDM 2019
  25. 🧠 Towards neural mixture recommender for long range dependent user sequences, WWW 2019
  26. πŸ“‘ Recommending what video to watch next: A multitask ranking system, RecSys 2019
  27. πŸ€” Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations, RecSys 2019
  28. Efficient Training on Very Large Corpora via Gramian Estimation, ICLR 2019
  29. 🧠 Latent Cross: Making Use of Context in Recurrent Recommender Systems, WSDM 2018
  30. Q&R: A two-stage approach toward interactive recommendation, KDD 2018
  31. Categorical-Attributes-Based Multi-Level Classification for Recommender Systems, RecSys 2018
  32. 🧠🎰 Deep Bayesian Bandits Showdown: an Empirical Comparison of Bayesian Deep Networks for Thompson Sampling, ICLR 2018
  33. πŸ“‘ The LambdaLoss Framework for Ranking Metric Optimization, CIKM 2018
  34. πŸ•΄πŸ”¬ Practical Diversified Recommendations on YouTube with Determinantal Point Processes, CIKM 2018
  35. Recommendations for All: Solving Thousands of Recommendation Problems Daily, ICDE 2018 (also describe user context representation by the actions taken)
  36. A Generic Coordinate Descent Framework for Learning from Implicit Feedback, WWW 2017
  37. 🧠 Deep & Cross Network for Ad Click Predictions, 2017
  38. πŸ•΄ Deep neural networks for YouTube recommendations, RecSys 2016; video
  39. πŸ•΄πŸ§  Wide & Deep Learning for Recommender Systems, DLRS 2016 (workshop on deep learning for recommender systems) – used for Google Play
  40. Q&R: A two-stage approach toward interactive recommendation, KDD 2018 (Google)
  41. Towards Conversational Recommender Systems, KDD 2016
  42. πŸ’΅ Focusing on the Long-term: It’s Good for Users and Business, KDD 2015
  43. πŸ’΅ Ad Click Prediction: a View from the Trenches, KDD 2013
  44. πŸ•΄ The YouTube video recommendation system, RecSys 2010
  45. Google news personalization: scalable online collaborative filtering, WWW 2007

Talks

  1. Reinforcement Learning for Recommender Systems: Some Challenges, ICML 2019

Blog posts

  1. Building Large Scale Recommenders using Cloud TPUs, 2022-10-07
  2. Advances in TF-Ranking, 2021-07-21
  3. Scholar Recommendations Reloaded! Fresher, More Relevant, Easier, 2021-02-12
  4. Announcing ScaNN: Efficient Vector Similarity Search, 2020-07-28
  5. Advanced machine learning helps Play Store users discover personalised apps, 2019-11-18

Software

  1. Trax: end-to-end DL library with focus on clear code and speed; used for transformer models, successor to tensor2tensor.
  2. rax, learning-to-rank framework for JAX, paper
  3. RecSim blog post
  4. ScaNN (Scalable Nearest Neighbors)
  5. TensorFlow Recommenders see also TF Recommenders SIG and TF recommender add-ons
  6. TensorFlow Ranking

External link