├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Data Artist 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # awesome-RecSys 2 | A curated list of awesome Recommender System - designed by **Jihoo Kim** 3 | 4 | ![RS](https://user-images.githubusercontent.com/50820635/85274861-7e0e3b00-b4ba-11ea-8cd3-2690ec55a67a.jpg) 5 | 6 | ### Table of Contents 7 | 1. [Books](https://github.com/jihoo-kim/awesome-RecSys#1-books) 8 | 2. [Conferences](https://github.com/jihoo-kim/awesome-RecSys#2-conferences) 9 | 3. [Researchers](https://github.com/jihoo-kim/awesome-RecSys#3-researchers) 10 | 4. [Papers](https://github.com/jihoo-kim/awesome-RecSys#4-papers) 11 | 5. [GitHub Repositories](https://github.com/jihoo-kim/awesome-RecSys#5-github-repositories) 12 | 6. [Useful Sites](https://github.com/jihoo-kim/awesome-RecSys#6-useful-sites) 13 | 7. [Youtube Videos](https://github.com/jihoo-kim/awesome-RecSys#7-youtube-videos) 14 | 8. [SlideShare PPT](https://github.com/jihoo-kim/awesome-RecSys#8-slideshare-ppt) 15 | 16 | ## 1. Books 17 | * [Recommender Systems: The Textbook](http://pzs.dstu.dp.ua/DataMining/recom/bibl/1aggarwal_c_c_recommender_systems_the_textbook.pdf) (2016, Charu Aggarwal) 18 | * [Recommender Systems Handbook 2nd Edition](https://edyaaleh.files.wordpress.com/2016/02/recommendersystemshandbook.pdf) (2015, Francesco Ricci) 19 | * [Recommender Systems Handbook 1st Edition](https://www.cse.iitk.ac.in/users/nsrivast/HCC/Recommender_systems_handbook.pdf) (2011, Francesco Ricci) 20 | * [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) 21 | 22 | ## 2. Conferences 23 | * [AAAI](https://www.aaai.org/) (AAAI Conference on Artificial Intelligence) 24 | * [CIKM](http://www.cikmconference.org/) (ACM International Conference on Information and Knowledge Management) 25 | * [CSCW](http://cscw.acm.org) (ACM Conference on Computer-Supported Cooperative Work & Social Computing) 26 | * [ICDM](http://icdm2019.bigke.org/) (IEEE International Conference on Data Mining) 27 | * [IJCAI](https://www.ijcai.org/) (International Joint Conference on Artificial Intelligence) 28 | * [ICLR](https://iclr.cc/) (International Conference on Learning Representations) 29 | * [ICML](https://icml.cc/) (International Conference on Machine Learning) 30 | * [IUI](https://iui.acm.org) (International Conference on Intelligent User Interfaces) 31 | * [NIPS](https://nips.cc/) (Neural Information Processing Systems) 32 | * [RecSys](https://recsys.acm.org/) (ACM Conference on Recommender Systems) 33 | * [SIGIR](https://sigir.org/) (ACM SIGIR Conference on Research and development in information retrieval) 34 | * [KDD](https://www.kdd.org/) (ACM SIGKDD International Conference on Knowledge discovery and data mining) 35 | * [VLDB](https://www.vldb.org/) (International Conference on Very Large Databases) 36 | * [WSDM](http://www.wsdm-conference.org/) (ACM International Conference on Web Search and Data Mining) 37 | * [WWW](https://www.iw3c2.org/) (International World Wide Web Conferences) 38 | 39 | ## 3. Researchers 40 | * [George Karypis](http://glaros.dtc.umn.edu/gkhome/index.php) (University of Minnesota) 41 | * [Joseph A. Konstan](http://konstan.umn.edu/) (University of Minnesota) 42 | * [Philip S. Yu](https://www.cs.uic.edu/PSYu) (University of Illinons at Chicago) 43 | * [Charu Aggarwal](http://www.charuaggarwal.net/) (IBM T. J. Watson Research Center) 44 | * [Martin Ester](http://www.sfu.ca/computing/people/faculty/martinester/people.html) (Simon Fraser University) 45 | * [Paul Resnick](http://presnick.people.si.umich.edu/) (University of Michigan) 46 | * [Peter Brusilovsky](http://www.pitt.edu/~peterb/) (University of Pittsburgh) 47 | * [Bamshad Mobasher](http://facweb.cs.depaul.edu/mobasher/) (DePaul University) 48 | * [Alexander Tuzhilin](http://people.stern.nyu.edu/atuzhili/) (New York University) 49 | * [Yehuda Koren](https://www.linkedin.com/in/yehuda-koren-8566147/) (Google) 50 | * [Barry Smyth](https://barrysmyth.me/) (University College Dublin) 51 | * [Lior Rokach](http://www.ise.bgu.ac.il/faculty/liorr/) (Ben-Gurion University of the Negev) 52 | * [Loren Terveen](https://www-users.cs.umn.edu/~terveen/) (University of Minnesota) 53 | * [Chris Volinsky](http://stats.research.att.com/volinsky/) (AT&T Labs) 54 | * [Ed H. Chi](https://sites.google.com/view/edchi/) (Google AI) 55 | * [Laks V.S. Lakshmanan](https://www.cs.ubc.ca/~laks/) (University of British Columbia) 56 | * [Badrul Sarwar](https://www.linkedin.com/in/bmsarwar/) (LinkedIn) 57 | * [Francesco Ricci](http://www.inf.unibz.it/~ricci/) (Free University of Bozen-Bolzano) 58 | * [Robin Burke](http://www.that-recsys-lab.net/) (University of Colorado, Boulder) 59 | * [Brent Smith](https://www.linkedin.com/in/brent-smith-2a1b8/) (Amazon) 60 | * [Greg Linden](http://glinden.blogspot.com/) (Amazon, Microsoft) 61 | * [Hao Ma](https://www.haoma.io/) (Facebook AI) 62 | * [Giovanni Semeraro](http://www.di.uniba.it/~swap/index.php?n=Membri.Semeraro) (University of Bari Aldo Moro) 63 | * [Dietmar Jannach](https://www.aau.at/en/ainf/research-groups/infsys/team/dietmar-jannach/) (University of Klagenfurt) 64 | 65 | ## 4. Papers 66 | * [Explainable Recommendation: A Survey and New Perspectives](https://arxiv.org/pdf/1804.11192) (2018, Yongfeng Zhang) 67 | * [Deep Learning based Recommender System: A Survey and New Perspectives](https://arxiv.org/pdf/1707.07435.pdf) (2018, Shuai Zhang) 68 | * [Collaborative Variational Autoencoder for Recommender Systems](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) (2017, Xiaopeng Li) 69 | * [Neural Collaborative Filtering](https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf) (2017, Xiangnan He) 70 | * [Deep Neural Networks for YouTube Recommendations](https://static.googleusercontent.com/media/research.google.com/ko//pubs/archive/45530.pdf) (2016, Paul Covington) 71 | * [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) (2016, Heng-Tze Cheng) 72 | * [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems](http://alicezheng.org/papers/wsdm16-cdae.pdf) (2016, Yao Wu) 73 | * [AutoRec: Autoencoders Meet Collaborative Filtering](http://users.cecs.anu.edu.au/~u5098633/papers/www15.pdf) (2015, Suvash Sedhain) 74 | * [Collaborative Deep Learning for Recommender Systems](http://www.wanghao.in/paper/KDD15_CDL.pdf) (2015, Hao Wang) 75 | * [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) 76 | * [Deep content-based music recommendation](https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf) (2013, Aaron van den Oord) 77 | * [Time-aware Point-of-interest Recommendation](https://www.ntu.edu.sg/home/axsun/paper/sun_sigir13quan.pdf) (2013, Quan Yuan) 78 | * [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) 79 | * [Context-Aware Recommender Systems for Learning: A Survey and Future Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6189308) (2012, Katrien Verbert) 80 | * [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) 81 | * [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) 82 | * [The YouTube Video Recommendation System](https://www.inf.unibz.it/~ricci/ISR/papers/p293-davidson.pdf) (2010, James Davidson) 83 | * [Matrix Factorization Techniques for Recommender Systems](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) (2009, Yehuda Koren) 84 | * [A Survey of Collaborative Filtering Techniques](http://downloads.hindawi.com/archive/2009/421425.pdf) (2009, Xiaoyuan Su) 85 | * [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) 86 | * [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) 87 | * [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) 88 | * [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) 89 | * [Flickr tag recommendation based on collective knowledge](http://www2008.org/papers/pdf/p327-sigurbjornssonA.pdf) (2008, Borkur Sigurbjornsson) 90 | * [Restricted Boltzmann machines for collaborative filtering](https://www.cs.toronto.edu/~rsalakhu/papers/rbmcf.pdf) (2007, Ruslan Salakhutdinov) 91 | * [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) 92 | * [Evaluating collaborative filtering recommender systems](https://grouplens.org/site-content/uploads/evaluating-TOIS-20041.pdf) (2004, Jonatan L. Herlocker) 93 | * [Amazon.com Recommendations: Item-to-Item Collaborative Filtering](https://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf) (2003, Greg Linden) 94 | * [Content-boosted collaborative filtering for improved recommendations](https://www.cs.utexas.edu/~ml/papers/cbcf-aaai-02.pdf) (2002, Prem Melville) 95 | * [Item-based collaborative filtering recommendation algorithms](http://www.ra.ethz.ch/cdstore/www10/papers/pdf/p519.pdf) (2001, Badrul Sarwar) 96 | * [Explaining collaborative filtering recommendations](https://grouplens.org/site-content/uploads/explain-CSCW-20001.pdf) (2000, Jonatan L. Herlocker) 97 | * [An algorithmic framework for performing collaborative filtering](http://files.grouplens.org/papers/algs.pdf) (1999, Jonathan L. Herlocker) 98 | * [Empirical analysis of predictive algorithms for collaborative filtering](https://arxiv.org/ftp/arxiv/papers/1301/1301.7363.pdf) (1998, John S. Breese) 99 | * [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) 100 | * [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) 101 | * [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) 102 | 103 | 104 | 105 | ## 5. GitHub Repositories 106 | * [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) 107 | * [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) 108 | * [RecommenderSystem-Paper](https://github.com/daicoolb/RecommenderSystem-Paper) ![](https://img.shields.io/github/stars/daicoolb/RecommenderSystem-Paper.svg?style=social) (Papers, Tools, Frameworks) 109 | * [RSPapers](https://github.com/hongleizhang/RSPapers) ![](https://img.shields.io/github/stars/hongleizhang/RSPapers.svg?style=social) (Papers) 110 | * [awesome-RecSys-papers](https://github.com/YuyangZhangFTD/awesome-RecSys-papers) ![](https://img.shields.io/github/stars/YuyangZhangFTD/awesome-RecSys-papers.svg?style=social) (Papers) 111 | * [DeepRec](https://github.com/cheungdaven/DeepRec) ![](https://img.shields.io/github/stars/cheungdaven/DeepRec.svg?style=social) (Tensorflow Codes) 112 | * [RecQ](https://github.com/Coder-Yu/RecQ) ![](https://img.shields.io/github/stars/Coder-Yu/RecQ.svg?style=social) (TensorFlow Codes) 113 | * [NeuRec](https://github.com/wubinzzu/NeuRec) ![](https://img.shields.io/github/stars/wubinzzu/NeuRec.svg?style=social) (TensorFlow Codes) 114 | * [RecNN](https://github.com/awarebayes/RecNN) ![](https://img.shields.io/github/stars//awarebayes/RecNN.svg?style=social) (PyTorch Codes) 115 | * [Surprise](https://github.com/NicolasHug/Surprise) ![](https://img.shields.io/github/stars/NicolasHug/Surprise.svg?style=social) (Python Library) 116 | * [LightFM](https://github.com/lyst/lightfm) ![](https://img.shields.io/github/stars/lyst/lightfm.svg?style=social) (Python Library) 117 | * [Spotlight](https://github.com/maciejkula/spotlight) ![](https://img.shields.io/github/stars/maciejkula/spotlight.svg?style=social) (Python Library) 118 | * [python-recsys](https://github.com/ocelma/python-recsys) ![](https://img.shields.io/github/stars/ocelma/python-recsys.svg?style=social) (Python Library) 119 | * [TensorRec](https://github.com/jfkirk/tensorrec) ![](https://img.shields.io/github/stars/jfkirk/tensorrec.svg?style=social) (Python Library) 120 | * [CaseRecommender](https://github.com/caserec/CaseRecommender) ![](https://img.shields.io/github/stars/caserec/CaseRecommender.svg?style=social) (Python Library) 121 | * [recommenders](https://github.com/microsoft/recommenders) ![](https://img.shields.io/github/stars/microsoft/recommenders.svg?style=social) (Jupyter Notebook Tutorial) 122 | 123 | ## 6. Useful Sites 124 | * [WikiCFP - Recommender System](http://www.wikicfp.com/cfp/call?conference=recommender%20systems) (Call For Papers of Conferences, Workshops and Journals - Recommender System) 125 | * [Guide2Research - Top CS Conference](http://www.guide2research.com/topconf/) (Top Computer Science Conferences) 126 | * [PapersWithCode - Recommender System](https://paperswithcode.com/task/recommendation-systems) (Papers with Code - Recommender System) 127 | * [Coursera - Recommender System](https://www.coursera.org/specializations/recommender-systems) (University of Minnesota - Joseph A. Konstan) 128 | 129 | ## 7. Youtube Videos 130 | * [RecSys Paper Presentation Videos](https://www.youtube.com/channel/UC2nEn-yNA1BtdDNWziphPGA/featured) (ACM RecSys) 131 | * [Building Recommender System with Machine Learning and AI](https://www.youtube.com/playlist?list=PLk9tco_9NSqfkr2Z0VdntKqufR5uDOezz) (Youtube SEO) 132 | * [Machine Learning - FULL COURSE | Andrew Ng | Stanford University](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) (Lecture 16.1 ~ Lecture 16.6) 133 | * [Mining Massive Datasets - FULL COURSE | Stanford University](https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV) (Lecture 41 ~ Lecture 45) 134 | * [Text Retrieval and Search Engines - FULL COURSE | UIUC](https://www.youtube.com/playlist?list=PLLssT5z_DsK8Jk8mpFc_RPzn2obhotfDO) (Lecture 38 ~ Lecture 42) 135 | * [Recommendation Systems - Learn Python for Data Science #3](https://www.youtube.com/watch?v=9gBC9R-msAk) (Siraj Raval) 136 | * [How does Netflix recommend movies? Matrix Factorization](https://www.youtube.com/watch?v=ZspR5PZemcs) (Luis Serrano) 137 | * [Machine Learning for Recommender Systems](https://www.youtube.com/watch?v=xBMGr08fowA&t=3m58s) (James Kirk Spotify) 138 | 139 | ## 8. SlideShare PPT 140 | * [Recommender system introduction](https://www.slideshare.net/xlvector/recommender-system-introduction-12551956) (Liang Xiang) 141 | * [Recommender system algorithm and architecture](https://www.slideshare.net/xlvector/recommender-system-algorithm-and-architecture-13098396) (Liang Xiang) 142 | * [How to build a recommender system?](https://www.slideshare.net/blueace/how-to-build-a-recommender-system-presentation) (Coen Stevens) 143 | * [Architecting recommender systems](https://www.slideshare.net/JamesKirk58/boston-ml-architecting-recommender-systems) (James Kirk Spotify) 144 | --------------------------------------------------------------------------------