Meta - Revision history

Zeno Gantner: /* Software */

2024-04-09T07:49:02Z

Software

Zeno Gantner: /* Papers */

2023-08-07T09:53:48Z

Papers

Zeno Gantner: /* Papers */

2023-08-07T09:53:36Z

Papers

Zeno Gantner: added and fixed blog post links

2023-05-22T15:40:46Z

added and fixed blog post links

Zeno Gantner at 12:09, 19 May 2023

2023-05-19T12:09:38Z

Zeno Gantner: /* Papers */

2023-05-19T12:08:07Z

Papers

Zeno Gantner: add paper ''Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models'', ISCA 2022 industry track

2023-04-26T16:19:35Z

add paper ''Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models'', ISCA 2022 industry track

Zeno Gantner: Category:Company

2023-02-24T10:41:41Z

Category:Company

Zeno Gantner at 10:40, 24 February 2023

2023-02-24T10:40:58Z

Zeno Gantner at 18:46, 4 December 2022

2022-12-04T18:46:47Z

@@ Line 42: / Line 42: @@
 == Software ==
 # [https://github.com/facebookresearch/dlrm DLRM recommender]: click probability model
 # [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.

@@ Line 33: / Line 33: @@
 # [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]]
 # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020
-# {{experimentation} [https://dominiccoey.github.io/assets/papers/experiment_splitting.pdf Improving Treatment Effect Estimators Through Experiment Splitting], WWW 2019
+# {{experimentation}} [https://dominiccoey.github.io/assets/papers/experiment_splitting.pdf Improving Treatment Effect Estimators Through Experiment Splitting], WWW 2019
 # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)
 # {{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]

@@ Line 33: / Line 33: @@
 # [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]]
 # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020
 # {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)
 # {{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]

@@ Line 3: / Line 3: @@
 == Blog posts ==
 # [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
 # [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
-# [https://research.fb.com/efficient-tuning-of-online-systems-using-bayesian-optimization/ Efficient tuning of online systems using Bayesian optimization]
+# [https://about.instagram.com/blog/announcements/shedding-more-light-on-how-instagram-works Shedding More Light on How Instagram Works], 2021-06-08
 # [https://engineering.fb.com/2020/12/17/ml-applications/diversified-recommendations/ On the value of diversified recommendations], 2020-12-17
 # [https://engineering.fb.com/2020/12/10/web/how-instagram-suggests-new-content/ How Instagram suggests new content], 2020-12-10
 # [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
 # {{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]
@@ Line 17: / Line 20: @@
 # [https://edoconti.medium.com/offline-policy-evaluation-run-fewer-better-a-b-tests-60ce8f93fa15 Offline Policy Evaluation: Run fewer, better A/B tests]
 # [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
 == Papers ==

@@ Line 22: / Line 22: @@
 # {{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
 # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]
-# [https://arxiv.org/pdf/2203.11014.pdf DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction], KDD 2022
+# {{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
 # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021
 # [https://research.fb.com/publications/preference-amplification-in-recommender-systems/ Preference Amplification in Recommender Systems], [[KDD 2021]]

@@ Line 20: / Line 20: @@
 == Papers ==
 # {{ops}}{{performance}} [https://dl.acm.org/doi/abs/10.1145/3534678.3539034 AutoShard: Automated Embedding Table Sharding for Recommender Systems], [[KDD 2022]]
 # https://research.fb.com/publications/the-decoupled-extended-kalman-filter-for-dynamic-exponential-family-factorization-models/, [[JMLR]], 2021
@@ Line 54: / Line 55: @@
 ** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]
 ** [https://github.com/Instagram Instagram]

← Older revision		Revision as of 10:41, 24 February 2023
Line 54:		Line 54:
	** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]		** [https://github.com/orgs/facebookexperimental/repositories?type=all FB experimental]
	** [https://github.com/Instagram Instagram]		** [https://github.com/Instagram Instagram]
		+
		+	[[Category:Company]]

← Older revision		Revision as of 10:40, 24 February 2023
Line 1:		Line 1:
−	'''Meta''' is the ~~parent~~ company of '''Facebook''', '''Instagram''', and '''WhatsApp'''.	+	'''Meta''' is the company behind '''Facebook''', '''Instagram''', and '''WhatsApp'''.

	== Blog posts ==		== Blog posts ==

@@ Line 26: / Line 26: @@
 # [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]]
 # {{fashion}} [https://arxiv.org/abs/2011.09663 Modeling Fashion Influence from Photos], IEEE Transactions on Multimedia, 2020
-# [https://arxiv.org/pdf/1906.02773.pdf One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers] [https://www.youtube.com/watch?v=oOgbHpjTwwA (video)]
+# {{neural}} [https://arxiv.org/pdf/1906.00091.pdf Deep Learning Recommendation Model for Personalization and Recommendation Systems], 2019 (DLRM)
 # {{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]
 # [https://arxiv.org/abs/1909.11810 Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems]
-# [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014
+# {{ads}} [http://quinonero.net/Publications/predicting-clicks-facebook.pdf Practical lessons from predicting clicks on ads at facebook], workshop 2014
 # [https://cs.stanford.edu/~jure/pubs/linkpred-wsdm11.pdf Supervised Random Walks: Predicting and Recommending Links in Social Networks], [[WSDM 2011]]