https://github.com/jihoo-kim/awesome-RecSys?fbclid=IwAR1m6OebmqO9mfLV1ta4OTihQc9Phw8WNS4zdr5IeT1X1OLWQvLk0Wz45f4
awesome-RecSys
A curated list of awesome Recommender System - designed by Jihoo Kim
Table of Contents
- Books
- Conferences
- Researchers
- Papers
- GitHub Repositories
- Useful Sites
- Youtube Videos
- SlideShare PPT
1. Books
- 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. Conferences
- 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. Researchers
- 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. Papers
- 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. Useful Sites
- WikiCFP - Recommender System (Call For Papers of Conferences, Workshops and Journals - Recommender System)
- Guide2Research - Top CS Conference (Top Computer Science Conferences)
- PapersWithCode - Recommender System (Papers with Code - Recommender System)
- Coursera - Recommender System (University of Minnesota - Joseph A. Konstan)
7. Youtube Videos
- RecSys Paper Presentation Videos (ACM RecSys)
- Building Recommender System with Machine Learning and AI (Youtube SEO)
- Machine Learning - FULL COURSE | Andrew Ng | Stanford University (Lecture 16.1 ~ Lecture 16.6)
- Mining Massive Datasets - FULL COURSE | Stanford University (Lecture 41 ~ Lecture 45)
- Text Retrieval and Search Engines - FULL COURSE | UIUC (Lecture 38 ~ Lecture 42)
- Recommendation Systems - Learn Python for Data Science #3 (Siraj Raval)
- How does Netflix recommend movies? Matrix Factorization (Luis Serrano)
8. SlideShare PPT
- Recommender system introduction (Liang Xiang)
- Recommender system algorithm and architecture (Liang Xiang)
- How to build a recommender system? (Coen Stevens)