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https://github.com/jihoo-kim/awesome-RecSys

A curated list of awesome Recommender System (Books, Conferences, Researchers, Papers, Github Repositories, Useful Sites, Youtube Videos)
https://github.com/jihoo-kim/awesome-RecSys

List: awesome-RecSys

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A curated list of awesome Recommender System (Books, Conferences, Researchers, Papers, Github Repositories, Useful Sites, Youtube Videos)

Awesome Lists containing this project

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# awesome-RecSys

A curated list of awesome Recommender System - designed by **Jihoo Kim**



![RS](https://user-images.githubusercontent.com/50820635/85274861-7e0e3b00-b4ba-11ea-8cd3-2690ec55a67a.jpg)



### Table of Contents

1. [Books](https://github.com/jihoo-kim/awesome-RecSys#1-books)

2. [Conferences](https://github.com/jihoo-kim/awesome-RecSys#2-conferences)

3. [Researchers](https://github.com/jihoo-kim/awesome-RecSys#3-researchers)

4. [Papers](https://github.com/jihoo-kim/awesome-RecSys#4-papers)

5. [GitHub Repositories](https://github.com/jihoo-kim/awesome-RecSys#5-github-repositories)

6. [Useful Sites](https://github.com/jihoo-kim/awesome-RecSys#6-useful-sites)

7. [Youtube Videos](https://github.com/jihoo-kim/awesome-RecSys#7-youtube-videos)

8. [SlideShare PPT](https://github.com/jihoo-kim/awesome-RecSys#8-slideshare-ppt)



## 1. Books

* [Recommender Systems: The Textbook](http://pzs.dstu.dp.ua/DataMining/recom/bibl/1aggarwal_c_c_recommender_systems_the_textbook.pdf) (2016, Charu Aggarwal)

* [Recommender Systems Handbook 2nd Edition](https://edyaaleh.files.wordpress.com/2016/02/recommendersystemshandbook.pdf) (2015, Francesco Ricci)

* [Recommender Systems Handbook 1st Edition](https://www.cse.iitk.ac.in/users/nsrivast/HCC/Recommender_systems_handbook.pdf) (2011, Francesco Ricci)

* [Recommender Systems An Introduction](https://github.com/singmiya/recsys/raw/master/Recommender%20Systems%20An%20Introduction.pdf) (2011, Dietmar Jannach) [slides](http://www.recommenderbook.net/teaching-material/slides)



## 2. Conferences

* [AAAI](https://www.aaai.org/) (AAAI Conference on Artificial Intelligence)

* [CIKM](http://www.cikmconference.org/) (ACM International Conference on Information and Knowledge Management)

* [CSCW](http://cscw.acm.org) (ACM Conference on Computer-Supported Cooperative Work & Social Computing)

* [ICDM](http://icdm2019.bigke.org/) (IEEE International Conference on Data Mining)

* [IJCAI](https://www.ijcai.org/) (International Joint Conference on Artificial Intelligence)

* [ICLR](https://iclr.cc/) (International Conference on Learning Representations)

* [ICML](https://icml.cc/) (International Conference on Machine Learning)

* [IUI](https://iui.acm.org) (International Conference on Intelligent User Interfaces)

* [NIPS](https://nips.cc/) (Neural Information Processing Systems)

* [RecSys](https://recsys.acm.org/) (ACM Conference on Recommender Systems)

* [SIGIR](https://sigir.org/) (ACM SIGIR Conference on Research and development in information retrieval)

* [KDD](https://www.kdd.org/) (ACM SIGKDD International Conference on Knowledge discovery and data mining)

* [VLDB](https://www.vldb.org/) (International Conference on Very Large Databases)

* [WSDM](http://www.wsdm-conference.org/) (ACM International Conference on Web Search and Data Mining)

* [WWW](https://www.iw3c2.org/) (International World Wide Web Conferences)



## 3. Researchers

* [George Karypis](http://glaros.dtc.umn.edu/gkhome/index.php) (University of Minnesota)

* [Joseph A. Konstan](http://konstan.umn.edu/) (University of Minnesota)

* [Philip S. Yu](https://www.cs.uic.edu/PSYu) (University of Illinons at Chicago)

* [Charu Aggarwal](http://www.charuaggarwal.net/) (IBM T. J. Watson Research Center)

* [Martin Ester](http://www.sfu.ca/computing/people/faculty/martinester/people.html) (Simon Fraser University)

* [Paul Resnick](http://presnick.people.si.umich.edu/) (University of Michigan)

* [Peter Brusilovsky](http://www.pitt.edu/~peterb/) (University of Pittsburgh)

* [Bamshad Mobasher](http://facweb.cs.depaul.edu/mobasher/) (DePaul University)

* [Alexander Tuzhilin](http://people.stern.nyu.edu/atuzhili/) (New York University)

* [Yehuda Koren](https://www.linkedin.com/in/yehuda-koren-8566147/) (Google)

* [Barry Smyth](https://barrysmyth.me/) (University College Dublin)

* [Lior Rokach](http://www.ise.bgu.ac.il/faculty/liorr/) (Ben-Gurion University of the Negev)

* [Loren Terveen](https://www-users.cs.umn.edu/~terveen/) (University of Minnesota)

* [Chris Volinsky](http://stats.research.att.com/volinsky/) (AT&T Labs)

* [Ed H. Chi](https://sites.google.com/view/edchi/) (Google AI)

* [Laks V.S. Lakshmanan](https://www.cs.ubc.ca/~laks/) (University of British Columbia)

* [Badrul Sarwar](https://www.linkedin.com/in/bmsarwar/) (LinkedIn)

* [Francesco Ricci](http://www.inf.unibz.it/~ricci/) (Free University of Bozen-Bolzano)

* [Robin Burke](http://www.that-recsys-lab.net/) (University of Colorado, Boulder)

* [Brent Smith](https://www.linkedin.com/in/brent-smith-2a1b8/) (Amazon)

* [Greg Linden](http://glinden.blogspot.com/) (Amazon, Microsoft)

* [Hao Ma](https://www.haoma.io/) (Facebook AI)

* [Giovanni Semeraro](http://www.di.uniba.it/~swap/index.php?n=Membri.Semeraro) (University of Bari Aldo Moro)

* [Dietmar Jannach](https://www.aau.at/en/ainf/research-groups/infsys/team/dietmar-jannach/) (University of Klagenfurt)



## 4. Papers

* [Explainable Recommendation: A Survey and New Perspectives](https://arxiv.org/pdf/1804.11192) (2018, Yongfeng Zhang)

* [Deep Learning based Recommender System: A Survey and New Perspectives](https://arxiv.org/pdf/1707.07435.pdf) (2018, Shuai Zhang)

* [Collaborative Variational Autoencoder for Recommender Systems](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) (2017, Xiaopeng Li)

* [Neural Collaborative Filtering](https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf) (2017, Xiangnan He)

* [Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/ko//pubs/archive/45530.pdf) (2016, Paul Covington)

* [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) (2016, Heng-Tze Cheng)

* [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems](http://alicezheng.org/papers/wsdm16-cdae.pdf) (2016, Yao Wu)

* [AutoRec: Autoencoders Meet Collaborative Filtering](http://users.cecs.anu.edu.au/~u5098633/papers/www15.pdf) (2015, Suvash Sedhain)

* [Collaborative Deep Learning for Recommender Systems](http://www.wanghao.in/paper/KDD15_CDL.pdf) (2015, Hao Wang)

* [Collaborative Filtering beyond the User-Item Matrix A Survey of the State of the Art and Future Challenges](https://github.com/daicoolb/RecommenderSystem-Paper/raw/master/Survey/Collaborative%20Filtering%20beyond%20the%20User-Item%20Matrix%20A%20Survey%20of%20the%20State%20of%20the%20Art%20and%20Future%20Challenges.pdf) (2014, Yue Shi)

* [Deep content-based music recommendation](https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf) (2013, Aaron van den Oord)

* [Time-aware Point-of-interest Recommendation](https://www.ntu.edu.sg/home/axsun/paper/sun_sigir13quan.pdf) (2013, Quan Yuan)

* [Location-based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/LocationRecommendation.pdf) (2012, Jie Bao)

* [Context-Aware Recommender Systems for Learning: A Survey and Future Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6189308) (2012, Katrien Verbert)

* [Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation](https://www.cse.cuhk.edu.hk/irwin.king.new/_media/presentations/p325.pdf) (2011, Mao Ye)

* [Recommender Systems with Social Regularization](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.352.9959&rep=rep1&type=pdf) (2011, Hao Ma)

* [The YouTube Video Recommendation System](https://www.inf.unibz.it/~ricci/ISR/papers/p293-davidson.pdf) (2010, James Davidson)

* [Matrix Factorization Techniques for Recommender Systems](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) (2009, Yehuda Koren)

* [A Survey of Collaborative Filtering Techniques](http://downloads.hindawi.com/archive/2009/421425.pdf) (2009, Xiaoyuan Su)

* [Collaborative Filtering with Temporal Dynamics](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.379.1951&rep=rep1&type=pdf) (2009, Yehuda Koren)

* [Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model](https://www.cs.rochester.edu/twiki/pub/Main/HarpSeminar/Factorization_Meets_the_Neighborhood-_a_Multifaceted_Collaborative_Filtering_Model.pdf) (2008, Yehuda Koren)

* [Collaborative Filtering for Implicit Feedback Datasets](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.167.5120&rep=rep1&type=pdf) (2008, Yifan Hu)

* [SoRec: social recommendation using probabilistic matrix factorization](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf) (2008, Hao Ma)

* [Flickr tag recommendation based on collective knowledge](http://www2008.org/papers/pdf/p327-sigurbjornssonA.pdf) (2008, Borkur Sigurbjornsson)

* [Restricted Boltzmann machines for collaborative filtering](https://www.cs.toronto.edu/~rsalakhu/papers/rbmcf.pdf) (2007, Ruslan Salakhutdinov)

* [Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions](http://pages.stern.nyu.edu/~atuzhili/pdf/TKDE-Paper-as-Printed.pdf) (2005, Gediminas Adomavicius)

* [Evaluating collaborative filtering recommender systems](https://grouplens.org/site-content/uploads/evaluating-TOIS-20041.pdf) (2004, Jonatan L. Herlocker)

* [Amazon.com Recommendations: Item-to-Item Collaborative Filtering](https://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf) (2003, Greg Linden)

* [Content-boosted collaborative filtering for improved recommendations](https://www.cs.utexas.edu/~ml/papers/cbcf-aaai-02.pdf) (2002, Prem Melville)

* [Item-based collaborative filtering recommendation algorithms](http://www.ra.ethz.ch/cdstore/www10/papers/pdf/p519.pdf) (2001, Badrul Sarwar)

* [Explaining collaborative filtering recommendations](https://grouplens.org/site-content/uploads/explain-CSCW-20001.pdf) (2000, Jonatan L. Herlocker)

* [An algorithmic framework for performing collaborative filtering](http://files.grouplens.org/papers/algs.pdf) (1999, Jonathan L. Herlocker)

* [Empirical analysis of predictive algorithms for collaborative filtering](https://arxiv.org/ftp/arxiv/papers/1301/1301.7363.pdf) (1998, John S. Breese)

* [Social information filtering: Algorithms for automating "word of mouth"](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.30.6583&rep=rep1&type=pdf) (1995, Upendra Shardanand)

* [GroupLens: an open architecture for collaborative filtering of netnews](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=0EE669AED51CA516AE8DD807338117DD?doi=10.1.1.53.9351&rep=rep1&type=pdf) (1994, Paul Resnick)

* [Using collaborative filtering to weave an information tapestry](http://bitsavers.org/pdf/xerox/parc/techReports/CSL-92-10_Using_Collaborative_Filtering_to_Weave_an_Information_Tapestry.pdf) (1992, David Goldberg)



## 5. GitHub Repositories

* [List_of_Recommender_Systems](https://github.com/grahamjenson/list_of_recommender_systems) ![](https://img.shields.io/github/stars/grahamjenson/list_of_recommender_systems.svg?style=social) (Software, Open Source, Academic, Benchmarking, Applications, Books)

* [Deep-Learning-for-Recommendation-Systems](https://github.com/robi56/Deep-Learning-for-Recommendation-Systems) ![](https://img.shields.io/github/stars/robi56/Deep-Learning-for-Recommendation-Systems.svg?style=social) (Papers, Blogs, Worshops, Tutorials, Software)

* [RecommenderSystem-Paper](https://github.com/daicoolb/RecommenderSystem-Paper) ![](https://img.shields.io/github/stars/daicoolb/RecommenderSystem-Paper.svg?style=social) (Papers, Tools, Frameworks)

* [RSPapers](https://github.com/hongleizhang/RSPapers) ![](https://img.shields.io/github/stars/hongleizhang/RSPapers.svg?style=social) (Papers)

* [awesome-RecSys-papers](https://github.com/YuyangZhangFTD/awesome-RecSys-papers) ![](https://img.shields.io/github/stars/YuyangZhangFTD/awesome-RecSys-papers.svg?style=social) (Papers)

* [DeepRec](https://github.com/cheungdaven/DeepRec) ![](https://img.shields.io/github/stars/cheungdaven/DeepRec.svg?style=social) (Tensorflow Codes)

* [RecQ](https://github.com/Coder-Yu/RecQ) ![](https://img.shields.io/github/stars/Coder-Yu/RecQ.svg?style=social) (TensorFlow Codes)

* [NeuRec](https://github.com/wubinzzu/NeuRec) ![](https://img.shields.io/github/stars/wubinzzu/NeuRec.svg?style=social) (TensorFlow Codes)

* [RecNN](https://github.com/awarebayes/RecNN) ![](https://img.shields.io/github/stars//awarebayes/RecNN.svg?style=social) (PyTorch Codes)

* [Surprise](https://github.com/NicolasHug/Surprise) ![](https://img.shields.io/github/stars/NicolasHug/Surprise.svg?style=social) (Python Library)

* [LightFM](https://github.com/lyst/lightfm) ![](https://img.shields.io/github/stars/lyst/lightfm.svg?style=social) (Python Library)

* [Spotlight](https://github.com/maciejkula/spotlight) ![](https://img.shields.io/github/stars/maciejkula/spotlight.svg?style=social) (Python Library)

* [python-recsys](https://github.com/ocelma/python-recsys) ![](https://img.shields.io/github/stars/ocelma/python-recsys.svg?style=social) (Python Library)

* [TensorRec](https://github.com/jfkirk/tensorrec) ![](https://img.shields.io/github/stars/jfkirk/tensorrec.svg?style=social) (Python Library)

* [CaseRecommender](https://github.com/caserec/CaseRecommender) ![](https://img.shields.io/github/stars/caserec/CaseRecommender.svg?style=social) (Python Library)

* [recommenders](https://github.com/microsoft/recommenders) ![](https://img.shields.io/github/stars/microsoft/recommenders.svg?style=social) (Jupyter Notebook Tutorial)



## 6. Useful Sites

* [WikiCFP - Recommender System](http://www.wikicfp.com/cfp/call?conference=recommender%20systems) (Call For Papers of Conferences, Workshops and Journals - Recommender System)

* [Guide2Research - Top CS Conference](http://www.guide2research.com/topconf/) (Top Computer Science Conferences)

* [PapersWithCode - Recommender System](https://paperswithcode.com/task/recommendation-systems) (Papers with Code - Recommender System)

* [Coursera - Recommender System](https://www.coursera.org/specializations/recommender-systems) (University of Minnesota - Joseph A. Konstan)



## 7. Youtube Videos

* [RecSys Paper Presentation Videos](https://www.youtube.com/channel/UC2nEn-yNA1BtdDNWziphPGA/featured) (ACM RecSys)

* [Building Recommender System with Machine Learning and AI](https://www.youtube.com/playlist?list=PLk9tco_9NSqfkr2Z0VdntKqufR5uDOezz) (Youtube SEO)

* [Machine Learning - FULL COURSE | Andrew Ng | Stanford University](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) (Lecture 16.1 ~ Lecture 16.6)

* [Mining Massive Datasets - FULL COURSE | Stanford University](https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV) (Lecture 41 ~ Lecture 45)

* [Text Retrieval and Search Engines - FULL COURSE | UIUC](https://www.youtube.com/playlist?list=PLLssT5z_DsK8Jk8mpFc_RPzn2obhotfDO) (Lecture 38 ~ Lecture 42)

* [Recommendation Systems - Learn Python for Data Science #3](https://www.youtube.com/watch?v=9gBC9R-msAk) (Siraj Raval)

* [How does Netflix recommend movies? Matrix Factorization](https://www.youtube.com/watch?v=ZspR5PZemcs) (Luis Serrano)

* [Machine Learning for Recommender Systems](https://www.youtube.com/watch?v=xBMGr08fowA&t=3m58s) (James Kirk Spotify)



## 8. SlideShare PPT

* [Recommender system introduction](https://www.slideshare.net/xlvector/recommender-system-introduction-12551956) (Liang Xiang)

* [Recommender system algorithm and architecture](https://www.slideshare.net/xlvector/recommender-system-algorithm-and-architecture-13098396) (Liang Xiang)

* [How to build a recommender system?](https://www.slideshare.net/blueace/how-to-build-a-recommender-system-presentation) (Coen Stevens)

* [Architecting recommender systems](https://www.slideshare.net/JamesKirk58/boston-ml-architecting-recommender-systems) (James Kirk Spotify)