Enjoy!
\n\n\nCredit: Source
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Amazon 2003Netflix 2012Spotify 2012Google 2013Spotify 2014Netflix 2015Telefonica 2016YouTube 2016Yahoo 2016Netflix 2016LinkedIn 2016Slack 2016Alibaba 2017Netflix 2017Twitter 2017Pinterest 201720th Century Fox 2018Netflix 2018Uber 2018Spotify 2018Alibaba 2019Alibaba 2019Alibaba 2019TripAdvisor 2019Facebook 2019Netflix 2019Uber 2019Spotify 2019Dropbox 2019Dropbox 2019LinkedIn 2019Amazon 2020Amazon 2020Alibaba 2020Alibaba 2020Alibaba 2020Alibaba 2020Alibaba 2020Alibaba 2020Spotify 2020Spotify 2020Spotify 2020Shopify 2020LinkedIn 2020LinkedIn 2020LinkedIn 2020ByteDance 2020Google 2020Google 2020Google 2020Tencent 2020Home Depot 2020Ikea 2020Pinterest 2020Pinterest 2020Pinterest 2020Pinterest 2020DoorDash 2020Gojek 2020Twitter 2021Google 2021ByteDance 2021Facebook 2021Pinterest 2021YouTube 2021Walmart 2021Amazon 2022Amazon 2022eBay 2022Peloton 2022Yelp 2022LinkedIn 2022Meta 2022DoorDash 2022Amazon 2016Lazada 2016Yahoo 2016LinkedIn 2016Twitter 2017Etsy 2017DoorDash 2017Airbnb 2018LinkedIn 2018LinkedIn 2018Uber 2018Alibaba 2018Alibaba 2018Amazon 2019Airbnb 2019LinkedIn 2019LinkedIn 2019LinkedIn 2019Gojek 2019Facebook 2019Alibaba 2019Alibaba 2019Google 2019Tokopedia 2019GrubHub 2019Baidu 2019Amazon 2020Airbnb 2020Airbnb 2020LinkedIn 2020LinkedIn 2020LinkedIn 2020LinkedIn 2020Microsoft 2020Traveloka 2020Wayfair 2020Alibaba 2020Pinterest 2020Pinterest 2020LinkedIn 2020Etsy 2020Allen Institute for AI 2020Salesforce 2020DoorDash 2020Spotify 2020Facebook 2020JD 2020Amazon 2021Amazon 2021Amazon 2021Alibaba 2021Etsy 2021Baidu 2021Stitch Fix 2021LinkedIn 2021Seznam 2021Pinterest 2021DoorDash 2022Enjoy!
\n\n\nCredit: Source
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Amazon 2003Netflix 2012Spotify 2012Google 2013Spotify 2014Netflix 2015Telefonica 2016YouTube 2016Yahoo 2016Netflix 2016LinkedIn 2016Slack 2016Alibaba 2017Netflix 2017Twitter 2017Pinterest 201720th Century Fox 2018Netflix 2018Uber 2018Spotify 2018Alibaba 2019Alibaba 2019Alibaba 2019TripAdvisor 2019Facebook 2019Netflix 2019Uber 2019Spotify 2019Dropbox 2019Dropbox 2019LinkedIn 2019Amazon 2020Amazon 2020Alibaba 2020Alibaba 2020Alibaba 2020Alibaba 2020Alibaba 2020Alibaba 2020Spotify 2020Spotify 2020Spotify 2020Shopify 2020LinkedIn 2020LinkedIn 2020LinkedIn 2020ByteDance 2020Google 2020Google 2020Google 2020Tencent 2020Home Depot 2020Ikea 2020Pinterest 2020Pinterest 2020Pinterest 2020Pinterest 2020DoorDash 2020Gojek 2020Twitter 2021Google 2021ByteDance 2021Facebook 2021Pinterest 2021YouTube 2021Walmart 2021Amazon 2022Amazon 2022eBay 2022Peloton 2022Yelp 2022LinkedIn 2022Meta 2022DoorDash 2022Amazon 2016Lazada 2016Yahoo 2016LinkedIn 2016Twitter 2017Etsy 2017DoorDash 2017Airbnb 2018LinkedIn 2018LinkedIn 2018Uber 2018Alibaba 2018Alibaba 2018Amazon 2019Airbnb 2019LinkedIn 2019LinkedIn 2019LinkedIn 2019Gojek 2019Facebook 2019Alibaba 2019Alibaba 2019Google 2019Tokopedia 2019GrubHub 2019Baidu 2019Amazon 2020Airbnb 2020Airbnb 2020LinkedIn 2020LinkedIn 2020LinkedIn 2020LinkedIn 2020Microsoft 2020Traveloka 2020Wayfair 2020Alibaba 2020Pinterest 2020Pinterest 2020LinkedIn 2020Etsy 2020Allen Institute for AI 2020Salesforce 2020DoorDash 2020Spotify 2020Facebook 2020JD 2020Amazon 2021Amazon 2021Amazon 2021Alibaba 2021Etsy 2021Baidu 2021Stitch Fix 2021LinkedIn 2021Seznam 2021Pinterest 2021DoorDash 2022Wow～ so cool. up! up! up!
", "rawMarkdown": "Wow～ so cool. up! up! up!", "votes": null }, { "id": "2098110", "postDate": "01/13/2023 08:28:50", "content": "Great sources! Thank you!
", "rawMarkdown": "Great sources! Thank you!", "votes": null }, { "id": "2456840", "postDate": "09/26/2023 12:54:00", "content": "This is amazing. Just what I was looking for. Thank you.
", "rawMarkdown": "This is amazing. Just what I was looking for. Thank you.", "votes": null } ], "comments": [ { "id": 2082879, "author_name": "shanggangli", "author_url": "", "post_date": "01/02/2023 04:07:22", "content": "Wow～ so cool. up! up! up!
", "votes": null, "replies": [] }, { "id": 2098110, "author_name": "xiaobojasonwei", "author_url": "", "post_date": "01/13/2023 08:28:50", "content": "Great sources! Thank you!
", "votes": null, "replies": [] }, { "id": 2456840, "author_name": "sanjibanibanerjee", "author_url": "", "post_date": "09/26/2023 12:54:00", "content": "This is amazing. Just what I was looking for. Thank you.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2070395": "### The Recommendation Systems Reading List You Should Have\n#### Huge list of resources for learning about recommendation systems\n\nEnjoy! \n\n> **Credit:** [Source](https://github.com/eugeneyan/applied-ml#recommendation)\n\n_____\n\n\n## Recommendation Systems\n1. [Amazon.com Recommendations: Item-to-Item Collaborative Filtering](https://ieeexplore.ieee.org/document/1167344) ([Paper](https://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf)) `Amazon` `2003`\n2. [Netflix Recommendations: Beyond the 5 stars (Part 1](https://netflixtechblog.com/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429) ([Part 2](https://netflixtechblog.com/netflix-recommendations-beyond-the-5-stars-part-2-d9b96aa399f5)) `Netflix` `2012`\n3. [How Music Recommendation Works — And Doesn’t Work](https://notes.variogr.am/2012/12/11/how-music-recommendation-works-and-doesnt-work/) `Spotify` `2012`\n4. [Learning to Rank Recommendations with the k -Order Statistic Loss](https://dl.acm.org/doi/10.1145/2507157.2507210) ([Paper](https://dl.acm.org/doi/pdf/10.1145/2507157.2507210)) `Google` `2013`\n5. [Recommending Music on Spotify with Deep Learning](https://benanne.github.io/2014/08/05/spotify-cnns.html) `Spotify` `2014`\n6. [Learning a Personalized Homepage](https://netflixtechblog.com/learning-a-personalized-homepage-aa8ec670359a) `Netflix` `2015`\n7. [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939) ([Paper](https://arxiv.org/pdf/1511.06939.pdf)) `Telefonica` `2016`\n8. [Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf) `YouTube` `2016`\n9. [E-commerce in Your Inbox: Product Recommendations at Scale](https://arxiv.org/abs/1606.07154) ([Paper](https://arxiv.org/pdf/1606.07154.pdf)) `Yahoo` `2016`\n10. [To Be Continued: Helping you find shows to continue watching on Netflix](https://netflixtechblog.com/to-be-continued-helping-you-find-shows-to-continue-watching-on-7c0d8ee4dab6) `Netflix` `2016`\n11. [Personalized Recommendations in LinkedIn Learning](https://engineering.linkedin.com/blog/2016/12/personalized-recommendations-in-linkedin-learning) `LinkedIn` `2016`\n12. [Personalized Channel Recommendations in Slack](https://slack.engineering/personalized-channel-recommendations-in-slack/) `Slack` `2016`\n13. [Recommending Complementary Products in E-Commerce Push Notifications](https://arxiv.org/abs/1707.08113) ([Paper](https://arxiv.org/pdf/1707.08113.pdf)) `Alibaba` `2017`\n14. [Artwork Personalization at Netflix](https://netflixtechblog.com/artwork-personalization-c589f074ad76) `Netflix` `2017`\n15. [A Meta-Learning Perspective on Cold-Start Recommendations for Items](https://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items) ([Paper](https://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items.pdf)) `Twitter` `2017`\n16. [Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time](https://arxiv.org/abs/1711.07601) ([Paper](https://arxiv.org/pdf/1711.07601.pdf)) `Pinterest` `2017`\n17. [How 20th Century Fox uses ML to predict a movie audience](https://cloud.google.com/blog/products/ai-machine-learning/how-20th-century-fox-uses-ml-to-predict-a-movie-audience) ([Paper](https://arxiv.org/abs/1810.08189)) `20th Century Fox` `2018`\n18. [Calibrated Recommendations](https://dl.acm.org/doi/10.1145/3240323.3240372) ([Paper](https://dl.acm.org/doi/pdf/10.1145/3240323.3240372)) `Netflix` `2018`\n19. [Food Discovery with Uber Eats: Recommending for the Marketplace](https://eng.uber.com/uber-eats-recommending-marketplace/) `Uber` `2018`\n20. [Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits](https://dl.acm.org/doi/10.1145/3240323.3240354) ([Paper](https://static1.squarespace.com/static/5ae0d0b48ab7227d232c2bea/t/5ba849e3c83025fa56814f45/1537755637453/BartRecSys.pdf)) `Spotify` `2018`\n21. [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://arxiv.org/abs/1905.06874) ([Paper](https://arxiv.org/pdf/1905.06874.pdf)) `Alibaba` `2019`\n22. [SDM: Sequential Deep Matching Model for Online Large-scale Recommender System](https://arxiv.org/abs/1909.00385) ([Paper](https://arxiv.org/pdf/1909.00385.pdf)) `Alibaba` `2019`\n23. [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://arxiv.org/abs/1904.08030) ([Paper](https://arxiv.org/pdf/1904.08030.pdf)) `Alibaba` `2019`\n24. [Personalized Recommendations for Experiences Using Deep Learning](https://www.tripadvisor.com/engineering/personalized-recommendations-for-experiences-using-deep-learning/) `TripAdvisor` `2019`\n25. [Powered by AI: Instagram’s Explore recommender system](https://ai.facebook.com/blog/powered-by-ai-instagrams-explore-recommender-system/) `Facebook` `2019`\n26. [Marginal Posterior Sampling for Slate Bandits](https://www.ijcai.org/proceedings/2019/308) ([Paper](https://www.ijcai.org/proceedings/2019/0308.pdf)) `Netflix` `2019`\n27. [Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations](https://eng.uber.com/uber-eats-graph-learning/) `Uber` `2019`\n28. [Music recommendation at Spotify](http://sigir.org/afirm2019/slides/16.%20Friday%20-%20Music%20Recommendation%20at%20Spotify%20-%20Ben%20Carterette.pdf) `Spotify` `2019`\n29. [Using Machine Learning to Predict what File you Need Next (Part 1)](https://dropbox.tech/machine-learning/content-suggestions-machine-learning) `Dropbox` `2019`\n30. [Using Machine Learning to Predict what File you Need Next (Part 2)](https://dropbox.tech/machine-learning/using-machine-learning-to-predict-what-file-you-need-next-part-2) `Dropbox` `2019`\n31. [Learning to be Relevant: Evolution of a Course Recommendation System](https://dl.acm.org/doi/pdf/10.1145/3357384.3357817) (**PAPER NEEDED**)`LinkedIn` `2019`\n32. [Temporal-Contextual Recommendation in Real-Time](https://www.amazon.science/publications/temporal-contextual-recommendation-in-real-time) ([Paper](https://assets.amazon.science/96/71/d1f25754497681133c7aa2b7eb05/temporal-contextual-recommendation-in-real-time.pdf)) `Amazon` `2020`\n33. [P-Companion: A Framework for Diversified Complementary Product Recommendation](https://www.amazon.science/publications/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation) ([Paper](https://assets.amazon.science/d5/16/3f7809974a899a11bacdadefdf24/p-companion-a-principled-framework-for-diversified-complementary-product-recommendation.pdf)) `Amazon` `2020`\n34. [Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction](https://arxiv.org/abs/2005.12981) ([Paper](https://arxiv.org/pdf/2005.12981.pdf)) `Alibaba` `2020`\n35. [TPG-DNN: A Method for User Intent Prediction with Multi-task Learning](https://arxiv.org/abs/2008.02122) ([Paper](https://arxiv.org/pdf/2008.02122.pdf)) `Alibaba` `2020`\n36. [PURS: Personalized Unexpected Recommender System for Improving User Satisfaction](https://dl.acm.org/doi/10.1145/3383313.3412238) ([Paper](https://dl.acm.org/doi/pdf/10.1145/3383313.3412238)) `Alibaba` `2020`\n37. [Controllable Multi-Interest Framework for Recommendation](https://arxiv.org/abs/2005.09347) ([Paper](https://arxiv.org/pdf/2005.09347)) `Alibaba` `2020`\n38. [MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction](https://arxiv.org/abs/2008.02974) ([Paper](https://arxiv.org/pdf/2008.02974.pdf)) `Alibaba` `2020`\n39. [ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation](https://arxiv.org/abs/2005.12002) ([Paper](https://arxiv.org/pdf/2005.12002.pdf)) `Alibaba` `2020`\n40. [For Your Ears Only: Personalizing Spotify Home with Machine Learning](https://engineering.atspotify.com/2020/01/16/for-your-ears-only-personalizing-spotify-home-with-machine-learning/) `Spotify` `2020`\n41. [Reach for the Top: How Spotify Built Shortcuts in Just Six Months](https://engineering.atspotify.com/2020/04/15/reach-for-the-top-how-spotify-built-shortcuts-in-just-six-months/) `Spotify` `2020`\n42. [Contextual and Sequential User Embeddings for Large-Scale Music Recommendation](https://dl.acm.org/doi/10.1145/3383313.3412248) ([Paper](https://dl.acm.org/doi/pdf/10.1145/3383313.3412248)) `Spotify` `2020`\n43. [The Evolution of Kit: Automating Marketing Using Machine Learning](https://engineering.shopify.com/blogs/engineering/evolution-kit-automating-marketing-machine-learning) `Shopify` `2020`\n44. [A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 1)](https://engineering.linkedin.com/blog/2020/course-recommendations-ai-part-one) `LinkedIn` `2020`\n45. [A Closer Look at the AI Behind Course Recommendations on LinkedIn Learning (Part 2)](https://engineering.linkedin.com/blog/2020/course-recommendations-ai-part-two) `LinkedIn` `2020`\n46. [Building a Heterogeneous Social Network Recommendation System](https://engineering.linkedin.com/blog/2020/building-a-heterogeneous-social-network-recommendation-system) `LinkedIn` `2020`\n47. [How TikTok recommends videos #ForYou](https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you) `ByteDance` `2020`\n48. [Zero-Shot Heterogeneous Transfer Learning from RecSys to Cold-Start Search Retrieval](https://arxiv.org/abs/2008.02930) ([Paper](https://arxiv.org/pdf/2008.02930.pdf)) `Google` `2020`\n49. [Improved Deep & Cross Network for Feature Cross Learning in Web-scale LTR Systems](https://arxiv.org/abs/2008.13535) ([Paper](https://arxiv.org/pdf/2008.13535.pdf)) `Google` `2020`\n50. [Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations](https://research.google/pubs/pub50257/) ([Paper](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/b9f4e78a8830fe5afcf2f0452862fb3c0d6584ea.pdf)) `Google` `2020`\n51. [Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation](https://arxiv.org/pdf/1906.04473.pdf) ([Paper](https://arxiv.org/pdf/1906.04473.pdf)) `Tencent` `2020`\n52. [A Case Study of Session-based Recommendations in the Home-improvement Domain](https://dl.acm.org/doi/10.1145/3383313.3412235) ([Paper](https://dl.acm.org/doi/pdf/10.1145/3383313.3412235)) `Home Depot` `2020`\n53. [Balancing Relevance and Discovery to Inspire Customers in the IKEA App](https://dl.acm.org/doi/10.1145/3383313.3411550) ([Paper](https://dl.acm.org/doi/pdf/10.1145/3383313.3411550)) `Ikea` `2020`\n54. [How we use AutoML, Multi-task learning and Multi-tower models for Pinterest Ads](https://medium.com/pinterest-engineering/how-we-use-automl-multi-task-learning-and-multi-tower-models-for-pinterest-ads-db966c3dc99e) `Pinterest` `2020`\n55. [Multi-task Learning for Related Products Recommendations at Pinterest](https://medium.com/pinterest-engineering/multi-task-learning-for-related-products-recommendations-at-pinterest-62684f631c12) `Pinterest` `2020`\n56. [Improving the Quality of Recommended Pins with Lightweight Ranking](https://medium.com/pinterest-engineering/improving-the-quality-of-recommended-pins-with-lightweight-ranking-8ff5477b20e3) `Pinterest` `2020`\n57. [Multi-task Learning and Calibration for Utility-based Home Feed Ranking](https://medium.com/pinterest-engineering/multi-task-learning-and-calibration-for-utility-based-home-feed-ranking-64087a7bcbad) `Pinterest` `2020`\n57. [Personalized Cuisine Filter Based on Customer Preference and Local Popularity](https://doordash.engineering/2020/01/27/personalized-cuisine-filter/) `DoorDash` `2020`\n58. [How We Built a Matchmaking Algorithm to Cross-Sell Products](https://www.gojek.io/blog/how-we-built-a-matchmaking-algorithm-to-cross-sell-products) `Gojek` `2020`\n59. [Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generation](https://arxiv.org/abs/2105.09293) ([Paper](https://arxiv.org/pdf/2105.09293.pdf)) `Twitter` `2021`\n60. [Self-supervised Learning for Large-scale Item Recommendations](https://arxiv.org/abs/2007.12865) ([Paper](https://arxiv.org/pdf/2007.12865.pdf)) `Google` `2021`\n61. [Deep Retrieval: End-to-End Learnable Structure Model for Large-Scale Recommendations](https://arxiv.org/abs/2007.07203) ([Paper](https://arxiv.org/pdf/2007.07203.pdf)) `ByteDance` `2021`\n62. [Using AI to Help Health Experts Address the COVID-19 Pandemic](https://ai.facebook.com/blog/using-ai-to-help-health-experts-address-the-covid-19-pandemic/) `Facebook` `2021`\n63. [Advertiser Recommendation Systems at Pinterest](https://medium.com/pinterest-engineering/advertiser-recommendation-systems-at-pinterest-ccb255fbde20) `Pinterest` `2021`\n64. [On YouTube's Recommendation System](https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/) `YouTube` `2021`\n65. [Mozrt, a Deep Learning Recommendation System Empowering Walmart Store Associates](https://medium.com/walmartglobaltech/mozrt-a-deep-learning-recommendation-system-empowering-walmart-store-associates-with-a-5d42c08d88da) `Walmart` `2021`\n65. [The Amazon Music conversational recommender is hitting the right notes](https://www.amazon.science/latest-news/how-amazon-music-uses-recommendation-system-machine-learning) `Amazon` `2022`\n66. [Personalized complementary product recommendation](https://www.amazon.science/publications/personalized-complementary-product-recommendation) ([Paper](https://assets.amazon.science/6c/d9/a0ec3eda4f0fb4312ce0ada41771/personalized-complementary-product-recommendation.pdf)) `Amazon` `2022`\n67. [Building a Deep Learning Based Retrieval System for Personalized Recommendations](https://tech.ebayinc.com/engineering/building-a-deep-learning-based-retrieval-system-for-personalized-recommendations/) `eBay` `2022`\n68. [How We Built: An Early-Stage Machine Learning Model for Recommendations](https://www.onepeloton.com/press/articles/how-we-built-machine-learning) `Peloton` `2022`\n69. [Beyond Matrix Factorization: Using hybrid features for user-business recommendations](https://engineeringblog.yelp.com/2022/04/beyond-matrix-factorization-using-hybrid-features-for-user-business-recommendations.html) `Yelp` `2022`\n70. [Improving job matching with machine-learned activity features](https://engineering.linkedin.com/blog/2022/improving-job-matching-with-machine-learned-activity-features-) `LinkedIn` `2022`\n71. [Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training](https://arxiv.org/abs/2108.09373v4) `Meta` `2022`\n72. [Homepage Recommendation with Exploitation and Exploration](https://doordash.engineering/2022/10/05/homepage-recommendation-with-exploitation-and-exploration/) `DoorDash` `2022`\n\n#### Search & Ranking\n\n1. [Amazon Search: The Joy of Ranking Products](https://www.amazon.science/publications/amazon-search-the-joy-of-ranking-products) ([Paper](https://assets.amazon.science/89/cd/34289f1f4d25b5857d776bdf04d5/amazon-search-the-joy-of-ranking-products.pdf), [Video](https://www.youtube.com/watch?v=NLrhmn-EZ88), [Code](https://github.com/dariasor/TreeExtra)) `Amazon` `2016`\n2. [How Lazada Ranks Products to Improve Customer Experience and Conversion](https://www.slideshare.net/eugeneyan/how-lazada-ranks-products-to-improve-customer-experience-and-conversion) `Lazada` `2016`\n3. [Ranking Relevance in Yahoo Search](https://www.kdd.org/kdd2016/subtopic/view/ranking-relevance-in-yahoo-search) ([Paper](https://www.kdd.org/kdd2016/papers/files/adf0361-yinA.pdf)) `Yahoo` `2016`\n4. [Learning to Rank Personalized Search Results in Professional Networks](https://arxiv.org/abs/1605.04624) ([Paper](https://arxiv.org/pdf/1605.04624.pdf)) `LinkedIn` `2016`\n5. [Using Deep Learning at Scale in Twitter’s Timelines](https://blog.twitter.com/engineering/en_us/topics/insights/2017/using-deep-learning-at-scale-in-twitters-timelines.html) `Twitter` `2017`\n6. [An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy](https://arxiv.org/abs/1711.01377) ([Paper](https://arxiv.org/pdf/1711.01377.pdf)) `Etsy` `2017`\n7. [Powering Search & Recommendations at DoorDash](https://doordash.engineering/2017/07/06/powering-search-recommendations-at-doordash/) `DoorDash` `2017`\n8. [Applying Deep Learning To Airbnb Search](https://arxiv.org/abs/1810.09591) ([Paper](https://arxiv.org/pdf/1810.09591.pdf)) `Airbnb` `2018`\n9. [In-session Personalization for Talent Search](https://arxiv.org/abs/1809.06488) ([Paper](https://arxiv.org/pdf/1809.06488.pdf)) `LinkedIn` `2018`\n10. [Talent Search and Recommendation Systems at LinkedIn](https://arxiv.org/abs/1809.06481) ([Paper](https://arxiv.org/pdf/1809.06481.pdf)) `LinkedIn` `2018`\n11. [Food Discovery with Uber Eats: Building a Query Understanding Engine](https://eng.uber.com/uber-eats-query-understanding/) `Uber` `2018`\n12. [Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search](https://arxiv.org/abs/1805.08524) ([Paper](https://arxiv.org/pdf/1805.08524.pdf)) `Alibaba` `2018`\n13. [Reinforcement Learning to Rank in E-Commerce Search Engine](https://arxiv.org/abs/1803.00710) ([Paper](https://arxiv.org/pdf/1803.00710.pdf)) `Alibaba` `2018`\n14. [Semantic Product Search](https://arxiv.org/abs/1907.00937) ([Paper](https://arxiv.org/pdf/1907.00937.pdf)) `Amazon` `2019`\n15. [Machine Learning-Powered Search Ranking of Airbnb Experiences](https://medium.com/airbnb-engineering/machine-learning-powered-search-ranking-of-airbnb-experiences-110b4b1a0789) `Airbnb` `2019`\n16. [Entity Personalized Talent Search Models with Tree Interaction Features](https://arxiv.org/abs/1902.09041) ([Paper](https://arxiv.org/pdf/1902.09041.pdf)) `LinkedIn` `2019`\n17. [The AI Behind LinkedIn Recruiter Search and recommendation systems](https://engineering.linkedin.com/blog/2019/04/ai-behind-linkedin-recruiter-search-and-recommendation-systems) `LinkedIn` `2019`\n18. [Learning Hiring Preferences: The AI Behind LinkedIn Jobs](https://engineering.linkedin.com/blog/2019/02/learning-hiring-preferences--the-ai-behind-linkedin-jobs) `LinkedIn` `2019`\n19. [The Secret Sauce Behind Search Personalisation](https://www.gojek.io/blog/the-secret-sauce-behind-search-personalisation) `Gojek` `2019`\n20. [Neural Code Search: ML-based Code Search Using Natural Language Queries](https://ai.facebook.com/blog/neural-code-search-ml-based-code-search-using-natural-language-queries/) `Facebook` `2019`\n21. [Aggregating Search Results from Heterogeneous Sources via Reinforcement Learning](https://arxiv.org/abs/1902.08882) ([Paper](https://arxiv.org/pdf/1902.08882.pdf)) `Alibaba` `2019`\n22. [Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search](https://dl.acm.org/doi/10.1145/3357384.3357809) `Alibaba` `2019`\n23. [Understanding Searches Better Than Ever Before](https://www.blog.google/products/search/search-language-understanding-bert/) ([Paper](https://arxiv.org/pdf/1810.04805.pdf)) `Google` `2019`\n24. [How We Used Semantic Search to Make Our Search 10x Smarter](https://medium.com/tokopedia-engineering/how-we-used-semantic-search-to-make-our-search-10x-smarter-bd9c7f601821) `Tokopedia` `2019`\n25. [Query2vec: Search query expansion with query embeddings](https://bytes.grubhub.com/search-query-embeddings-using-query2vec-f5931df27d79) `GrubHub` `2019`\n26. [MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Search](http://research.baidu.com/Public/uploads/5d12eca098d40.pdf) `Baidu` `2019`\n27. [Why Do People Buy Seemingly Irrelevant Items in Voice Product Search?](https://www.amazon.science/publications/why-do-people-buy-irrelevant-items-in-voice-product-search) ([Paper](https://assets.amazon.science/f7/48/0562b2c14338a0b76ccf4f523fa5/why-do-people-buy-irrelevant-items-in-voice-product-search.pdf)) `Amazon` `2020`\n28. [Managing Diversity in Airbnb Search](https://arxiv.org/abs/2004.02621) ([Paper](https://arxiv.org/pdf/2004.02621.pdf)) `Airbnb` `2020`\n29. [Improving Deep Learning for Airbnb Search](https://arxiv.org/abs/2002.05515) ([Paper](https://arxiv.org/pdf/2002.05515.pdf)) `Airbnb` `2020`\n30. [Quality Matches Via Personalized AI for Hirer and Seeker Preferences](https://engineering.linkedin.com/blog/2020/quality-matches-via-personalized-ai) `LinkedIn` `2020`\n31. [Understanding Dwell Time to Improve LinkedIn Feed Ranking](https://engineering.linkedin.com/blog/2020/understanding-feed-dwell-time) `LinkedIn` `2020`\n32. [Ads Allocation in Feed via Constrained Optimization](https://dl.acm.org/doi/abs/10.1145/3394486.3403391) ([Paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403391), [Video](https://crossminds.ai/video/5f33697a0576dd25aef288ea/)) `LinkedIn` `2020`\n33. [Understanding Dwell Time to Improve LinkedIn Feed Ranking](https://engineering.linkedin.com/blog/2020/understanding-feed-dwell-time) `LinkedIn` `2020`\n34. [AI at Scale in Bing](https://blogs.bing.com/search/2020_05/AI-at-Scale-in-Bing) `Microsoft` `2020`\n35. [Query Understanding Engine in Traveloka Universal Search](https://medium.com/traveloka-engineering/query-understanding-engine-in-traveloka-universal-search-410ad3895db7) `Traveloka` `2020`\n36. [Bayesian Product Ranking at Wayfair](https://tech.wayfair.com/data-science/2020/01/bayesian-product-ranking-at-wayfair) `Wayfair` `2020`\n37. [COLD: Towards the Next Generation of Pre-Ranking System](https://arxiv.org/abs/2007.16122) ([Paper](https://arxiv.org/pdf/2007.16122.pdf)) `Alibaba` `2020`\n38. [Shop The Look: Building a Large Scale Visual Shopping System at Pinterest](https://dl.acm.org/doi/abs/10.1145/3394486.3403372) ([Paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403372), [Video](https://crossminds.ai/video/5f3369790576dd25aef288d7/)) `Pinterest` `2020`\n39. [Driving Shopping Upsells from Pinterest Search](https://medium.com/pinterest-engineering/driving-shopping-upsells-from-pinterest-search-d06329255402) `Pinterest` `2020`\n40. [GDMix: A Deep Ranking Personalization Framework](https://engineering.linkedin.com/blog/2020/gdmix--a-deep-ranking-personalization-framework) ([Code](https://github.com/linkedin/gdmix)) `LinkedIn` `2020`\n41. [Bringing Personalized Search to Etsy](https://codeascraft.com/2020/10/29/bringing-personalized-search-to-etsy/) `Etsy` `2020`\n42. [Building a Better Search Engine for Semantic Scholar](https://medium.com/ai2-blog/building-a-better-search-engine-for-semantic-scholar-ea23a0b661e7) `Allen Institute for AI` `2020`\n43. [Query Understanding for Natural Language Enterprise Search](https://arxiv.org/abs/2012.06238) ([Paper](https://arxiv.org/pdf/2012.06238.pdf)) `Salesforce` `2020`\n44. [Things Not Strings: Understanding Search Intent with Better Recall](https://doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/) `DoorDash` `2020`\n45. [Query Understanding for Surfacing Under-served Music Content](https://research.atspotify.com/publications/query-understanding-for-surfacing-under-served-music-content/) ([Paper](https://labtomarket.files.wordpress.com/2020/08/cikm2020.pdf)) `Spotify` `2020`\n46. [Embedding-based Retrieval in Facebook Search](https://arxiv.org/abs/2006.11632) ([Paper](https://arxiv.org/pdf/2006.11632.pdf)) `Facebook` `2020`\n47. [Towards Personalized and Semantic Retrieval for E-commerce Search via Embedding Learning](https://arxiv.org/abs/2006.02282) ([Paper](https://arxiv.org/pdf/2006.02282.pdf)) `JD` `2020`\n48. [QUEEN: Neural query rewriting in e-commerce](https://www.amazon.science/publications/queen-neural-query-rewriting-in-e-commerce) ([Paper](https://assets.amazon.science/f9/78/dda8f1e143dba8ca96e43ec487c6/queen-neural-query-rewriting-in-ecommerce.pdf)) `Amazon` `2021`\n49. [Using Learning-to-rank to Precisely Locate Where to Deliver Packages](https://www.amazon.science/blog/using-learning-to-rank-to-precisely-locate-where-to-deliver-packages) ([Paper](https://www.amazon.science/publications/getting-your-package-to-the-right-place-supervised-machine-learning-for-geolocation)) `Amazon` `2021`\n50. [Seasonal relevance in e-commerce search](https://www.amazon.science/publications/seasonal-relevance-in-e-commerce-search) ([Paper](https://assets.amazon.science/ac/5e/d47612a846d6bec15738d7c8ab40/seasonal-relevance-in-ecommerce-search.pdf)) `Amazon` `2021`\n51. [Graph Intention Network for Click-through Rate Prediction in Sponsored Search](https://arxiv.org/abs/2103.16164) ([Paper](https://arxiv.org/pdf/2103.16164.pdf)) `Alibaba` `2021`\n52. [How We Built A Context-Specific Bidding System for Etsy Ads](https://codeascraft.com/2021/03/23/how-we-built-a-context-specific-bidding-system-for-etsy-ads/) `Etsy` `2021`\n53. [Pre-trained Language Model based Ranking in Baidu Search](https://arxiv.org/abs/2105.11108) ([Paper](https://arxiv.org/pdf/2105.11108.pdf)) `Baidu` `2021`\n54. [Stitching together spaces for query-based recommendations](https://multithreaded.stitchfix.com/blog/2021/08/13/stitching-together-spaces-for-query-based-recommendations/) `Stitch Fix` `2021`\n55. [Deep Natural Language Processing for LinkedIn Search Systems](https://arxiv.org/abs/2108.08252) ([Paper](https://arxiv.org/pdf/2108.08252.pdf)) `LinkedIn` `2021`\n56. [Siamese BERT-based Model for Web Search Relevance Ranking](https://arxiv.org/abs/2112.01810) ([Paper](https://arxiv.org/pdf/2112.01810.pdf), [Code](https://github.com/seznam/DaReCzech)) `Seznam` `2021`\n57. [SearchSage: Learning Search Query Representations at Pinterest](https://medium.com/pinterest-engineering/searchsage-learning-search-query-representations-at-pinterest-654f2bb887fc) `Pinterest` `2021`\n58. [3 Changes to Expand DoorDash’s Product Search Beyond Delivery](https://doordash.engineering/2022/05/10/3-changes-to-expand-doordashs-product-search/) `DoorDash` `2022`", "2082879": "Wow～ so cool. up! up! up!", "2098110": "Great sources! Thank you!", "2456840": "This is amazing. Just what I was looking for. Thank you." }, "source": "meta" }