Amazon - Revision history

Zeno Gantner: /* Papers */

2023-07-17T09:54:03Z

Papers

Zeno Gantner: /* Papers */

2023-05-22T12:59:17Z

Papers

Zeno Gantner: Created page with "'''Amazon''' is the largest online retailer as well, with its subsidiary Amazon Web Services (AWS), the largest cloud provider in the Western world. They were also one of the..."

2022-12-02T23:29:53Z

Created page with "'''Amazon''' is the largest online retailer as well, with its subsidiary Amazon Web Services (AWS), the largest cloud provider in the Western world. They were also one of the..."

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'''Amazon''' is the largest online retailer as well, with its subsidiary Amazon Web Services (AWS), the largest cloud provider in the Western world.
They were also one of the first, if not the first, commercial user of recommendation systems.
AWS also offers [[recommendations as a service]] with their product [[AWS Personalize]].

== Papers ==

# [https://www.amazon.science/publications?f0=0000016e-2ff2-d205-a5ef-affb543e0000&s=0 all search and information retrieval publications by Amazon]
# [http://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Amazon-Recommendations.pdf Amazon.com Recommendations: Item-to-Item Collaborative Filtering], IEEE Internet Computing, 2003
# [https://dl.acm.org/doi/pdf/10.1145/2764468.2764488 Estimating the Causal Impact of Recommendation Systems from Observational Data], EC 2015
# [https://dl.acm.org/doi/10.1145/2783258.2788579 One-Pass Ranking Models for Low-Latency Product Recommendations], KDD 2015
# {{visual}} [https://dl.acm.org/doi/10.1145/2959100.2959171 Adaptive, Personalized Diversity for Visual Discovery], RecSys 2016 (best short paper)
# [https://www.amazon.science/publications/diversifying-music-recommendations Diversifying Music Recommendations], ICML 2016
# [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
# [https://www.amazon.science/publications/recommending-product-sizes-to-customers Recommending Product Sizes to Customers], RecSys 2017
# {{production}} [https://www.amazon.science/publications/two-decades-of-recommender-systems-at-amazon-com Two Decades of Recommender Systems at Amazon.com], 2017
# [https://www.amazon.science/publications/intent-based-relevance-estimation-from-click-logs Intent Based Relevance Estimation from Click Logs], CIKM 2017
# [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
# {{ltor}}{{bandits}} [https://arxiv.org/abs/2004.13106 Learning to Rank in the Position Based Model with Bandit Feedback] (Amazon Music)
# {{bandits}} [https://arxiv.org/abs/2004.13576 A Linear Bandit for Seasonal Environments] (Amazon Music)
# [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]
# {{visual}} [https://dl.acm.org/doi/pdf/10.1145/2959100.2959171 Adaptive, personalized diversity for visual discovery], RecSys 2016
# {{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]]
# [https://dl.acm.org/doi/pdf/10.1145/3219819.3219891 Buy It Again: Modeling Repeat Purchase Recommendations], [[KDD 2018]]
# [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]
# {{search}} [https://www.amazon.science/publications/learning-robust-models-for-e-commerce-product-search Learning Robust Models for e-Commerce Product Search]
# {{search}} [https://www.amazon.science/publications/treating-cold-start-in-product-search-by-priors Treating Cold Start in Product Search by Priors]
# {{performance}} [https://www.amazon.science/publications/scalable-feature-selection-for-multitask-gradient-boosted-trees Scalable Feature Selection for (Multitask) Gradient Boosted Trees]
# {{ltor}} [https://www.amazon.science/publications/multi-objective-relevance-ranking Multi-objective Relevance Ranking via Constrained Optimization]
# [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]
# {{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)
# {{performance}} [https://arxiv.org/pdf/1901.04321.pdf Large-scale Collaborative Filtering with Product Embeddings], 2019
# [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]]
# {{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]
# {{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]
# [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
# [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]]
# {{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]]
# {{fashion}} [https://www.amazon.science/publications/fashion-outfit-complementary-item-retrieval Fashion Outfit Complementary Item Retrieva], [[CVPR 2020]]
# [https://arxiv.org/pdf/2012.08489.pdf Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization], 2020
# [https://arxiv.org/pdf/2012.06678.pdf TabTransformer: Tabular Data Modeling Using Contextual Embeddings], arXiv preprint, 2020
# {{bandits}} [https://www.amazon.science/publications/learning-from-extreme-bandit-feedback Learning from eXtreme bandit feedback]
# {{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
# {{search}} [https://www.amazon.science/publications/seasonal-relevance-in-e-commerce-search Seasonal relevance in e-commerce search], [[CIKM 2021]]
# {{fashion}} [https://arxiv.org/pdf/2207.12033.pdf Contrastive Learning for Interactive Recommendation in Fashion], [[SIGIR 2022]]

== Blog posts ==

* [https://www.amazon.science/blog/applying-pecos-to-product-retrieval-and-text-autocompletion Applying PECOS to product retrieval and text autocompletion], 2021-08-26
* {{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
* {{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
* [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
* [https://aws.amazon.com/blogs/media/whats-new-in-recommender-systems/ What’s new in recommender systems], 2020-11-17
* {{bandits}} [https://www.amazon.science/blog/a-general-approach-to-solving-bandit-problems A general approach to solving bandit problems], 2020-10
* [https://www.amazon.science/the-history-of-amazons-recommendation-algorithm The history of Amazon’s recommendation algorithm], 2019-11-22
* [https://www.amazon.science/blog/improving-complementary-product-recommendations Improving complementary-product recommendations]
* [https://www.amazon.science/blog/cvpr-deep-learning-has-more-gas-in-the-tank CVPR: Deep learning has more gas in the tank]
* [https://www.amazon.science/conferences-and-events/cvpr-2020 Amazon publications at CVPR 2020]
* {{visual}} [https://www.amazon.science/blog/how-computer-vision-will-help-amazon-customers-shop-online How computer vision will help Amazon customers shop online]

== Articles ''about'' Amazon ==

# {{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]

== Software ==
* 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.

== External links ==

* http://www.amazon.com/
* [https://www.amazon.science/ science blog]
* [https://aws.amazon.com/personalize/ AWS Personalize]
* [https://github.com/amzn Amazon GitHub]
* [https://github.com/aws AWS GitHub]
* [https://github.com/awslabs awslabs GitHub]

[[Category:Company]]

@@ Line 9: / Line 9: @@
 # [http://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Amazon-Recommendations.pdf Amazon.com Recommendations: Item-to-Item Collaborative Filtering], IEEE Internet Computing, 2003
 # [https://dl.acm.org/doi/pdf/10.1145/2764468.2764488 Estimating the Causal Impact of Recommendation Systems from Observational Data], EC 2015
-# [https://dl.acm.org/doi/10.1145/2783258.2788579 One-Pass Ranking Models for Low-Latency Product Recommendations], KDD 2015
+# [https://dl.acm.org/doi/10.1145/2783258.2788579 One-Pass Ranking Models for Low-Latency Product Recommendations], [[KDD 2015]]
-# {{visual}} [https://dl.acm.org/doi/10.1145/2959100.2959171 Adaptive, Personalized Diversity for Visual Discovery], RecSys 2016 (best short paper)
+# {{visual}} [https://dl.acm.org/doi/pdf/10.1145/2959100.2959171 Adaptive, personalized diversity for visual discovery], [[RecSys 2016]] (best short paper)
-# [https://www.amazon.science/publications/diversifying-music-recommendations Diversifying Music Recommendations], ICML 2016
+# [https://www.amazon.science/publications/diversifying-music-recommendations Diversifying Music Recommendations], [[ICML 2016]]
-# [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
+# [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]]
 # [https://www.amazon.science/publications/recommending-product-sizes-to-customers Recommending Product Sizes to Customers], RecSys 2017
 # {{production}} [https://www.amazon.science/publications/two-decades-of-recommender-systems-at-amazon-com Two Decades of Recommender Systems at Amazon.com], 2017
-# [https://www.amazon.science/publications/intent-based-relevance-estimation-from-click-logs Intent Based Relevance Estimation from Click Logs], CIKM 2017
+# [https://www.amazon.science/publications/intent-based-relevance-estimation-from-click-logs Intent Based Relevance Estimation from Click Logs], [[CIKM 2017]]
 # [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
 # {{ltor}}{{bandits}} [https://arxiv.org/abs/2004.13106 Learning to Rank in the Position Based Model with Bandit Feedback] (Amazon Music)
 # {{bandits}} [https://arxiv.org/abs/2004.13576 A Linear Bandit for Seasonal Environments] (Amazon Music)
 # [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]
-# {{visual}} [https://dl.acm.org/doi/pdf/10.1145/2959100.2959171 Adaptive, personalized diversity for visual discovery], RecSys 2016
+# {{bandits}} [https://dl.acm.org/doi/10.1145/3097983.3098184 An Efficient Bandit Algorithm for Realtime Multivariate Optimization], [[KDD 2017]]
 # {{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]]
 # [https://dl.acm.org/doi/pdf/10.1145/3219819.3219891 Buy It Again: Modeling Repeat Purchase Recommendations], [[KDD 2018]]
 # [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]
@@ Line 45: / Line 46: @@
 # {{fashion}} [https://arxiv.org/pdf/2207.12033.pdf Contrastive Learning for Interactive Recommendation in Fashion], [[SIGIR 2022]]
 # {{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]]
 == Blog posts ==