推薦系統資料整理

做者：清華阿羅
連接：https://zhuanlan.zhihu.com/p/67959931
來源：知乎
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網上看到不錯的推薦系統資料整理，分享給你們，包括書籍、會議、相關研究人員、論文和一些開源的代碼，以下：

1. 書籍

• Recommender Systems: The Textbook (2016, Charu Aggarwal)
• Recommender Systems Handbook 2nd Edition (2015, Francesco Ricci)
• Recommender Systems Handbook 1st Edition (2011, Francesco Ricci)
• Recommender Systems An Introduction (2011, Dietmar Jannach) slides

2. 會議

• AAAI (AAAI Conference on Artificial Intelligence)
• CIKM (ACM International Conference on Information and Knowledge Management)
• CSCW (ACM Conference on Computer-Supported Cooperative Work & Social Computing)
• ICDM (IEEE International Conference on Data Mining)
• IJCAI (International Joint Conference on Artificial Intelligence)
• ICLR (International Conference on Learning Representations)
• ICML (International Conference on Machine Learning)
• IUI (International Conference on Intelligent User Interfaces)
• NIPS (Neural Information Processing Systems)
• RecSys (ACM Conference on Recommender Systems)
• SDM (SIAM International Conference on Data Mining)
• SIGIR (ACM SIGIR Conference on Research and development in information retrieval)
• SIGKDD (ACM SIGKDD International Conference on Knowledge discovery and data mining)
• SIGMOD (ACM SIGMOD International Conference on Management of Data)
• VLDB (International Conference on Very Large Databases)
• WSDM (ACM International Conference on Web Search and Data Mining)
• WWW (International World Wide Web Conferences)

3. 研究人員

• George Karypis (University of Minnesota)
• Joseph A. Konstan (University of Minnesota)
• Philip S. Yu (University of Illinons at Chicago)
• Charu Aggarwal (IBM T. J. Watson Research Center)
• Martin Ester (Simon Fraser University)
• Paul Resnick (University of Michigan)
• Peter Brusilovsky (University of Pittsburgh)
• Bamshad Mobasher (DePaul University)
• Alexander Tuzhilin (New York University)
• Yehuda Koren (Google)
• Barry Smyth (University College Dublin)
• Lior Rokach (Ben-Gurion University of the Negev)
• Loren Terveen (University of Minnesota)
• Chris Volinsky (AT&T Labs)
• Ed H. Chi (Google AI)
• Laks V.S. Lakshmanan (University of British Columbia)
• Badrul Sarwar (LinkedIn)
• Francesco Ricci (Free University of Bozen-Bolzano)
• Robin Burke (University of Colorado, Boulder)
• Brent Smith (Amazon)
• Greg Linden (Amazon, Microsoft)
• Hao Ma (Facebook AI)
• Giovanni Semeraro (University of Bari Aldo Moro)
• Dietmar Jannach (University of Klagenfurt)

4. 論文

• Explainable Recommendation: A Survey and New Perspectives (2018, Yongfeng Zhang)
• Deep Learning based Recommender System: A Survey and New Perspectives (2018, Shuai Zhang)
• Collaborative Variational Autoencoder for Recommender Systems (2017, Xiaopeng Li)
• Neural Collaborative Filtering (2017, Xiangnan He)
• Deep Neural Networks for YouTube Recommendations (2016, Paul Covington)
• Wide & Deep Learning for Recommender Systems (2016, Heng-Tze Cheng)
• Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (2016, Yao Wu)
• AutoRec: Autoencoders Meet Collaborative Filtering (2015, Suvash Sedhain)
• Collaborative Deep Learning for Recommender Systems (2015, Hao Wang)
• Collaborative Filtering beyond the User-Item Matrix A Survey of the State of the Art and Future Challenges (2014, Yue Shi)
• Deep content-based music recommendation (2013, Aaron van den Oord)
• Time-aware Point-of-interest Recommendation (2013, Quan Yuan)
• Location-based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data (2012, Jie Bao)
• Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (2012, Katrien Verbert)
• Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation (2011, Mao Ye)
• Recommender Systems with Social Regularization (2011, Hao Ma)
• The YouTube Video Recommendation System (2010, James Davidson)
• Matrix Factorization Techniques for Recommender Systems (2009, Yehuda Koren)
• A Survey of Collaborative Filtering Techniques (2009, Xiaoyuan Su)
• Collaborative Filtering with Temporal Dynamics (2009, Yehuda Koren)
• Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model (2008, Yehuda Koren)
• Collaborative Filtering for Implicit Feedback Datasets (2008, Yifan Hu)
• SoRec: social recommendation using probabilistic matrix factorization (2008, Hao Ma)
• Flickr tag recommendation based on collective knowledge (2008, Borkur Sigurbjornsson)
• Restricted Boltzmann machines for collaborative filtering (2007, Ruslan Salakhutdinov)
• Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions(2005, Gediminas Adomavicius)
• Evaluating collaborative filtering recommender systems (2004, Jonatan L. Herlocker)
• Amazon.com Recommendations: Item-to-Item Collaborative Filtering (2003, Greg Linden)
• Content-boosted collaborative filtering for improved recommendations (2002, Prem Melville)
• Item-based collaborative filtering recommendation algorithms (2001, Badrul Sarwar)
• Explaining collaborative filtering recommendations (2000, Jonatan L. Herlocker)
• An algorithmic framework for performing collaborative filtering (1999, Jonathan L. Herlocker)
• Empirical analysis of predictive algorithms for collaborative filtering (1998, John S. Breese)
• Social information filtering: Algorithms for automating "word of mouth" (1995, Upendra Shardanand)
• GroupLens: an open architecture for collaborative filtering of netnews (1994, Paul Resnick)
• Using collaborative filtering to weave an information tapestry (1992, David Goldberg)

5. GitHub Repositories

• List_of_Recommender_Systems (Software, Open Source, Academic, Benchmarking, Applications, Books)
• Deep-Learning-for-Recommendation-Systems (Papers, Blogs, Worshops, Tutorials, Software)
• RecommenderSystem-Paper (Papers, Tools, Frameworks)
• RSPapers (Papers)
• awesome-RecSys-papers (Papers)
• DeepRec (Tensorflow Codes)
• RecQ (Tensorflow Codes)
• NeuRec (Tensorflow Codes)
• Surprise (Python Library)
• LightFM (Python Library)
• Spotlight (Python Library)
• python-recsys (Python Library)
• TensorRec (Python Library)
• CaseRecommender (Python Library)
• recommenders (Jupyter Notebook Tutorial)

6. 有用站點

• WikiCFP (Call For Papers of Conferences, Workshops and Journals - Recommender System)
• Guide2Research (Top Computer Science Conferences)
• PapersWithCode (Papers with Code - Recommender System)

7.新增

• https://github.com/microsoft/recommenders（微軟發佈的推薦代碼）
• shenweichen/DeepCTR （這篇文章涉及到ctr預估的經典方法，也有一些實現，很是適合作推薦的朋友）

參考：

1.https://github.com/jihoo-kim/awesome-RecSys?fbclid=IwAR1m6OebmqO9mfLV1ta4OTihQc9Phw8WNS4zdr5IeT1X1OLWQvLk0Wz45f4