{"id":18369517,"url":"https://github.com/chihming/competitive-recsys","last_synced_at":"2026-01-31T07:31:08.296Z","repository":{"id":43211126,"uuid":"123529349","full_name":"chihming/competitive-recsys","owner":"chihming","description":"A collection of resources for Recommender Systems (RecSys)","archived":false,"fork":false,"pushed_at":"2021-12-13T11:54:34.000Z","size":79,"stargazers_count":536,"open_issues_count":0,"forks_count":115,"subscribers_count":35,"default_branch":"master","last_synced_at":"2025-06-25T01:14:13.414Z","etag":null,"topics":["collaborative-filtering","recommendation-algorithm","recsys"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/chihming.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-03-02T04:16:31.000Z","updated_at":"2025-06-05T01:54:04.000Z","dependencies_parsed_at":"2022-09-09T23:21:19.285Z","dependency_job_id":null,"html_url":"https://github.com/chihming/competitive-recsys","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/chihming/competitive-recsys","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chihming%2Fcompetitive-recsys","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chihming%2Fcompetitive-recsys/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chihming%2Fcompetitive-recsys/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chihming%2Fcompetitive-recsys/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/chihming","download_url":"https://codeload.github.com/chihming/competitive-recsys/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chihming%2Fcompetitive-recsys/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28933212,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-31T04:05:25.756Z","status":"ssl_error","status_checked_at":"2026-01-31T04:02:35.005Z","response_time":128,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["collaborative-filtering","recommendation-algorithm","recsys"],"created_at":"2024-11-05T23:29:41.060Z","updated_at":"2026-01-31T07:31:08.279Z","avatar_url":"https://github.com/chihming.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n\n# competitive-recsys\nA collection of resources for Recommender Systems (RecSys)\n\n# Recommendation Algorithms\n\n- Basic of Recommender Systems\n  - [Wikipedia](https://en.wikipedia.org/wiki/Recommender_system)\n- Nearest Neighbor Search\n  - [Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)\n  - [sklearn.neighbors](http://scikit-learn.org/stable/modules/neighbors.html)\n  - [Benchmarks of approximate nearest neighbor libraries](https://github.com/erikbern/ann-benchmarks)\n- Classic Matrix Facotirzation\n  - [Matrix Factorization: A Simple Tutorial and Implementation in Python](http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/)\n  - [Matrix Factorization Techiques for Recommendaion Systems](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf)\n- Singular Value Decomposition (SVD)\n  - [Wikipedia](https://en.wikipedia.org/wiki/Singular-value_decomposition)\n- SVD++\n  - [Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model](http://www.cs.rochester.edu/twiki/pub/Main/HarpSeminar/Factorization_Meets_the_Neighborhood-_a_Multifaceted_Collaborative_Filtering_Model.pdf)\n- Content-based CF / Context-aware CF\n  - there are so many ...\n- Advanced Matrix Factorization\n  - [Probabilistic Matrix Factorization](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf)\n  - [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](https://dl.acm.org/citation.cfm?id=2911489)\n  - [Collaborative Filtering for Implicit Feedback Datasets](http://ieeexplore.ieee.org/document/4781121/)\n  - [Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence](https://dl.acm.org/citation.cfm?id=2959182)\n- Factorization Machine\n  - [Factorization Machines](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf)\n  - [Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/citation.cfm?id=2959134)\n- Sparse LInear Method (SLIM)\n  - [SLIM: Sparse Linear Methods for Top-N Recommender Systems](http://glaros.dtc.umn.edu/gkhome/node/774)\n  - [Global and Local SLIM](http://glaros.dtc.umn.edu/gkhome/node/1192)\n- Learning to Rank\n  - [Wikipedia](https://en.wikipedia.org/wiki/Learning_to_rank)\n  - [BPR: Bayesian personalized ranking from implicit feedback](https://dl.acm.org/citation.cfm?id=1795167)\n  - [WSABIE: Scaling Up To Large Vocabulary Image Annotation](http://www.thespermwhale.com/jaseweston/papers/wsabie-ijcai.pdf)\n  - [Top-1 Feedback](http://proceedings.mlr.press/v38/chaudhuri15.pdf)\n  - [k-order statistic loss](http://www.ee.columbia.edu/~ronw/pubs/recsys2013-kaos.pdf)\n  - [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://dl.acm.org/citation.cfm?id=3015834)\n  - [The LambdaLoss Framework for Ranking Metric Optimization](https://dl.acm.org/citation.cfm?id=3271784)\n- Cold-start\n  - [Deep content-based music recommendation](https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation)\n  - [DropoutNet: Addressing Cold Start in Recommender Systems](https://papers.nips.cc/paper/7081-dropoutnet-addressing-cold-start-in-recommender-systems)\n- Network Embedding\n  - [awesome-network-embedding](https://github.com/chihming/awesome-network-embedding)\n  - [Item2vec](https://arxiv.org/abs/1603.04259)\n  - [entity2rec](https://dl.acm.org/citation.cfm?id=3109889)\n  - [Collaborative Similarity Embedding for Recommender Systems](https://dl.acm.org/doi/10.1145/3308558.3313493)\n- Sequential-based\n  - [Factorizing Personalized Markov Chains for Next-Basket Recommendation](https://dl.acm.org/citation.cfm?id=1772773)\n  - [Learning Hierarchical Representation Model for NextBasket Recommendation]\n  - [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation]\n  - [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939)\n  - [Self-Attentive Sequential Recommendation]\n  - [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer]\n  - [S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization]\n  - [Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation]\n- CapsNet-based\n  - [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall]\n  - [Controllable Multi-Interest Framework for Recommendation]\n- Translation Embedding\n  - [Translation-based Recommendation](https://dl.acm.org/citation.cfm?id=3109882)\n  - [Translation-based Factorization Machines for Sequential Recommendation](https://dl.acm.org/citation.cfm?id=3240356)\n- Graph-Convolution-based\n  - [GraphSAGE: Inductive Representation Learning on Large Graphs](https://dl.acm.org/doi/10.5555/3294771.3294869)\n  - [PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://arxiv.org/abs/1806.01973)\n  - [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://arxiv.org/abs/2002.02126)\n- Knowledge-Graph-based\n  - [Collaborative knowledge base embedding for recommender systems](https://dl.acm.org/doi/10.1145/2939672.2939673)\n  - [Knowledge Graph Convolutional Networks for Recommender Systems](https://dl.acm.org/citation.cfm?id=3313417)\n  - [KGAT: Knowledge Graph Attention Network for Recommendation](https://dl.acm.org/authorize.cfm?key=N688414)\n  - [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://www.comp.nus.edu.sg/~xiangnan/papers/www19-KGRec.pdf)\n  - [Ripplenet: Propagating user preferences on the knowledge graph for recommender systems](https://dl.acm.org/doi/10.1145/3269206.3271739)\n- Rating-Prediction-based\n  - [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://dl.acm.org/doi/10.1145/3018661.3018665)\n  - [Neural Attentional Rating Regression with Review-level Explanations](http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf)\n  - [Convolutional Matrix Factorization for Document Context-Aware Recommendation](https://dl.acm.org/doi/10.1145/2959100.2959165)\n  - [A Context-Aware User-Item Representation Learning for Item Recommendation](https://dl.acm.org/doi/10.1145/3298988)\n  - [DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation](https://dl.acm.org/doi/10.1145/3292500.3330906)\n- Muti-task Learning\n  - [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)\n  - [Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236)\n- Deep Learning (early DL research)\n  - [Deep Neural Networks for YouTube Recommendations](https://ai.google/research/pubs/pub45530)\n  - [Deep Learning based Recommender System: A Survey and New Perspectives](https://arxiv.org/abs/1707.07435)\n  - [Neural Collaborative Filtering](https://dl.acm.org/citation.cfm?id=3052569)\n  - [Collaborative Deep Learning for Recommender Systems](http://www.wanghao.in/CDL.htm)\n  - [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems](https://dl.acm.org/citation.cfm?id=2835837)\n  - [Collaborative recurrent autoencoder: recommend while learning to fill in the blanks](https://dl.acm.org/citation.cfm?id=3157143)\n  - [TensorFlow Wide \u0026 Deep Learning](https://www.tensorflow.org/tutorials/wide_and_deep)\n  - [Deep Neural Networks for YouTube Recommendations](https://research.google.com/pubs/pub45530.html)\n  - [Collaborative Memory Network for Recommendation Systems](https://arxiv.org/abs/1804.10862)\n  - [Variational Autoencoders for Collaborative Filtering](https://dl.acm.org/citation.cfm?id=3186150)\n  - [Neural Graph Collaborative Filtering](https://dl.acm.org/doi/10.1145/3331184.3331267)\n\n\n# Public Available Datasets\n- [Recommender Systems Datasets](https://cseweb.ucsd.edu/~jmcauley/datasets.html)\n- [GroupLens](https://grouplens.org/)\n  - [MovieLens](https://grouplens.org/datasets/movielens/)\n  - [HetRec2011](https://grouplens.org/datasets/hetrec-2011/)\n  - [WikiLens](https://grouplens.org/datasets/wikilens/)\n  - [Book-Crossing](https://grouplens.org/datasets/book-crossing/)\n  - [Jester](https://grouplens.org/datasets/jester/)\n  - [EachMovie](https://grouplens.org/datasets/eachmovie/)\n- [Amazon Product Data](http://jmcauley.ucsd.edu/data/amazon/)\n  - Books, Electronics, Movies, etc.\n- [SNAP Datasets](https://snap.stanford.edu/data/index.html)\n- [#nowplaying Dataset](http://dbis-nowplaying.uibk.ac.at/)\n- [Last.fm Datasets](http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html)\n- [Million Song Dataset](https://labrosa.ee.columbia.edu/millionsong/)\n- [Frappe](http://baltrunas.info/research-menu/frappe)\n- [Yahoo! Webscope Program](https://webscope.sandbox.yahoo.com/)\n  - music ratings, movie ratings, etc.\n- [Yelp Dataset Challenge](https://www.yelp.com/dataset/challenge)\n- [MovieTweetings](https://github.com/sidooms/MovieTweetings)\n- [Foursquare](https://archive.org/details/201309_foursquare_dataset_umn)\n- [Epinions](http://jmcauley.ucsd.edu/data/epinions)\n- [Google Local](http://jmcauley.ucsd.edu/data/googlelocal/)\n  - location, phone number, time, rating, addres, GPS, etc.\n- [CiteUlike-t](http://www.wanghao.in/CDL.htm)\n- [LibimSeTi](http://www.occamslab.com/petricek/data/)\n- [Scholarly Paper Recommendation Datasets](http://www.comp.nus.edu.sg/~sugiyama/SchPaperRecData.html)\n- [Netflix Prize Data Set](http://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a)\n- [FilmTrust,CiaoDVD](https://www.librec.net/datasets.html)\n- [Chicago Entree](http://archive.ics.uci.edu/ml/datasets/Entree+Chicago+Recommendation+Data)\n- [Douban](http://socialcomputing.asu.edu/datasets/Douban)\n- [BibSonomy](https://www.kde.cs.uni-kassel.de/bibsonomy/dumps)\n- [Delicious](http://www.dai-labor.de/en/competence_centers/irml/datasets/)\n- [Foursquare](https://archive.org/details/201309_foursquare_dataset_umn)\n- [SmartMedia Adressa News Dataset](http://reclab.idi.ntnu.no/dataset/?fbclid=IwAR22dSZ_b3xMOypDGKFYCR6dkIPIADi70x09dYHU0IyV3pUX56B1foYjvIw)\n- [MACLab LJ Datasets](http://mac.citi.sinica.edu.tw/LJ#.Ww_hbFOFNE5)\n- Kaggle::Datasets\n  - [Steam Video Games](https://www.kaggle.com/tamber/steam-video-games/data)\n  - [Anime Recommendations Database](https://www.kaggle.com/CooperUnion/anime-recommendations-database)\n- [UCSD Book Graph](https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/home?authuser=0)\n\n# Open Sources\n- [libFM](http://www.libfm.org/) - Factorization Machine Library\n- [fastFM](https://github.com/ibayer/fastFM) - A Library for Factorization Machines\n- [LIBFFM](https://www.csie.ntu.edu.tw/~cjlin/libffm/) - A Library for Field-aware Factorization Machines\n- [lightfm](https://github.com/lyst/lightfm) - A Python implementation of LightFM, a hybrid recommendation algorithm\n- [LIBMF](https://www.csie.ntu.edu.tw/~cjlin/libmf/) - A Matrix-factorization Library for Recommender Systems\n- [LibRec](https://www.librec.net/index.html) - A Leading Java Library for Recommender Systems\n- [LensKit](http://lenskit.org/) - Open-Source Tools for Recommender Systems\n- [Surprise](https://github.com/NicolasHug/Surprise) - A Python scikit building and analyzing recommender systems\n- [MyMediaLite Recommender System Library](http://www.mymedialite.net/index.html)\n- [QMF](https://github.com/quora/qmf) - A matrix factorization library\n- [proNet-core](https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network\n- [Rival](http://rival.recommenders.net/) - An open source Java toolkit for recommender system evaluation\n- [TensorRec](https://github.com/jfkirk/tensorrec) - A TensorFlow recommendation algorithm and framework in Python\n- [OpenRec](http://openrec.ai/index.html) - An open-source and modular library for neural network-inspired recommendation algorithms\n- [spotlight](https://github.com/maciejkula/spotlight) - Deep recommender models using PyTorch.\n- [Recoder](https://github.com/amoussawi/recoder) - Large scale training of factorization models for Collaborative Filtering with PyTorch.\n- [Ranking](https://github.com/tensorflow/ranking) - TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform.\n- [RecNN](https://github.com/awarebayes/RecNN) - Reinforced Recommendation toolkit build around pytorch 1.4\n- [recommenders](https://github.com/microsoft/recommenders) - This repository contains examples and best practices for building recommendation systems.\n\n# Common Evaluation Metric\n- Precision and Recall\n  - [Wikipedia](https://en.wikipedia.org/wiki/Precision_and_recall)\n- Mean Average Precision (MAP)\n  - [Wikipedia](https://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision)\n- ROC Curve / Area under the curve\n  - [Wikipedia](https://en.wikipedia.org/wiki/Receiver_operating_characteristic)\n- Normalized Discounted Cumulative Gain (NDCG)\n  - [Wikipedia](https://en.wikipedia.org/wiki/Discounted_cumulative_gain)\n- Mean Absolute Error (MAE)\n  - [Wikipedia](https://en.wikipedia.org/wiki/Mean_absolute_error)\n- Root Mean Square Error (RMSE) \n  - [Wikipedia](https://en.wikipedia.org/wiki/Root-mean-square_deviation)\n- Novelty and Diversity\n  - [Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation](https://dl.acm.org/citation.cfm?id=1944341)\n- Beyond accuracy\n  - [Beyond accuracy: evaluating recommender systems by coverage and serendipity](https://dl.acm.org/citation.cfm?id=1864761)\n  \n  \n# Related Github links\n- [List of Recommender Systems](https://github.com/grahamjenson/list_of_recommender_systems) - A List of Recommender Systems and Resources\n- [Recommendation and Ratings Public Data Sets For Machine Learning](https://gist.github.com/entaroadun/1653794)\n- [RecommenderSystem-Paper](https://github.com/daicoolb/RecommenderSystem-Paper)\n- [Must-read papers on Recommender System](https://github.com/hongleizhang/RSPapers)\n- [knowledge graph, user-item profile, recommendation system](https://github.com/BaeSeulki/WhySoMuch)\n- [Must-read Papers on Recommendation System and CTR Prediction](https://github.com/imsheridan/DeepRec)\n\n# Textbooks\n- [Programming Collective Intelligence](http://shop.oreilly.com/product/9780596529321.do)\n\n# Online Courses\n- [Recommender Systems Specialization](https://zh-tw.coursera.org/specializations/recommender-systems), University of Minnesota\n- [Introduction to Recommender Systems: Non-Personalized and Content-Based](https://zh-tw.coursera.org/learn/recommender-systems-introduction), University of Minnesota\n\n# RecSys-related Competitions\n- [Kaggle](https://www.kaggle.com/) - product recommendations, hotel recommendations, job recommendations, etc.\n- ACM RecSys Challenge\n- [WSDM Cup 2018](https://wsdm-cup-2018.kkbox.events/)\n- [KDD Cup 2020 Challenges](https://tianchi.aliyun.com/competition/entrance/231785/introduction)\n- [Million Song Dataset Challenge](https://www.kaggle.com/c/msdchallenge)\n- [Netflix Prize](https://www.netflixprize.com/)\n\n# Tutorials\n- RecSys tutorials\n  - [2014](https://recsys.acm.org/recsys14/tutorials/)\n  - [2015](https://recsys.acm.org/recsys15/tutorials/)\n  - [2016](https://recsys.acm.org/recsys16/tutorials/)\n  - [2017](https://recsys.acm.org/recsys17/tutorials/)\n  - [2018](https://recsys.acm.org/recsys18/tutorials/)\n- [Kdd 2014 Tutorial - the recommender problem revisited](https://www.slideshare.net/xamat/kdd-2014-tutorial-the-recommender-problem-revisited)\n\n# Articles\n- [Matrix Factorization: A Simple Tutorial and Implementation in Python](http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/)\n \n- [Introduction to Reinforcement Learning for News Recommendation](https://towardsdatascience.com/reinforcement-learning-ddpg-and-td3-for-news-recommendation-d3cddec26011)\n\n# Conferences\n- [RecSys – ACM Recommender Systems](https://recsys.acm.org/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchihming%2Fcompetitive-recsys","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchihming%2Fcompetitive-recsys","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchihming%2Fcompetitive-recsys/lists"}