推荐系统相关资源介绍（书籍、代码、综述、教程等内容）

嘿，记得给“机器学习与推荐算法”添加星标

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本文整理了推荐系统相关的经典 书籍、相关会议、Github仓库、顶会教程、综述论文、视频介绍以及论文笔记等 内容。大家如果有觉得不错的资源也可以一起来完善他们~

书籍

• Recommender Systems: The Textbook (2016, Charu Aggarwal)
• Recommender Systems Handbook 2nd Edition (2015, Francesco Ricci)
• Practical Recommender Systems （Kim Falk）
• Recommender Systems An Introduction (2011, Dietmar Jannach) slides
• 《推荐系统实践》
• 《深度学习推荐系统》

会议

• 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)
• SIGIR (ACM SIGIR Conference on Research and development in information retrieval)
• KDD (ACM SIGKDD International Conference on Knowledge discovery and data mining)
• VLDB (International Conference on Very Large Databases)
• WSDM (ACM International Conference on Web Search and Data Mining)
• WWW (International World Wide Web Conferences)

仓库

• List_of_Recommender_Systems  (Software, Open Source, Academic, Benchmarking, Applications, Books) https://github.com/grahamjenson/list_of_recommender_systems
• RSPapers  (Papers) https://github.com/hongleizhang/RSPapers
• awesome-RecSys-papers  (Papers) https://github.com/YuyangZhangFTD/awesome-RecSys-papers
• DeepRec  (Tensorflow Codes) https://github.com/cheungdaven/DeepRec
• RecQ  (TensorFlow Codes) https://github.com/Coder-Yu/QRec
• NeuRec  (TensorFlow Codes) https://github.com/wubinzzu/NeuRec
• RecNN  (PyTorch Codes) https://github.com/awarebayes/RecNN
• Surprise  (Python Library) https://github.com/NicolasHug/Surprise
• LightFM  (Python Library) https://github.com/lyst/lightfm
• Spotlight  (Python Library) https://github.com/maciejkula/spotlight
• python-recsys  (Python Library) https://github.com/ocelma/python-recsys
• TensorRec  (Python Library) https://github.com/jfkirk/tensorrec
• CaseRecommender  (Python Library) https://github.com/caserec/CaseRecommender
• recommenders  (Jupyter Notebook Tutorial) https://github.com/microsoft/recommenders
• TorchRec （Pytorch Codes） https://github.com/pytorch/torchrec
• TFRec https://github.com/tensorflow/recommenders
• RecBole （Pytorch Codes） https://github.com/RUCAIBox/RecBole
• MTReclib （PyTorch Codes） https://github.com/easezyc/Multitask-Recommendation-Library
• ReChorus （Pytorch Codes） https：//github.com/THUwangcy/ReChorus

教程

https://github.com/hongleizhang/RSPapers#tutorials

Deepak et al. Recommender Problems for Web Application. ICML, 2011.

Bart et al. Explaining the user experience of recommender systems. Recsys, 2012.

Ester et al. Recommendation in Social Networks. Recsys, 2013.

Ivan et al. Cross-Domain Recommender Systems. Recsys, 2014.

Steck et al. Interactive Recommender Systems. Recsys, 2015.

Frank et al. Real-Time Recommendation of Streamed Data. Recsys, 2015.

Boratto et al. Group Recommender Systems. Recsys, 2016.

Alex et al. Deep Learning for Recommender Systems. Recsys, 2017.

Bart et al. -Privacy for Recommender Systems. Recsys, 2017.

Xu et al. Deep learning for matching in search and recommendation. SIGIR, 2018.

Massimo et al. Sequence-Aware Recommenders. Recsys, 2018.

Wang et al. Learning and Reasoning on Graph for Recommendation. CIKM, 2019.

Sonie et al. Concept to Code: Deep Learning for Multitask Recommendation. Recsys, 2019.

Michael at al. Fairness & Discrimination in Recommendation & Retrieval. Recsys, 2019.

Yang et al. Deep Transfer Learning for Search and Recommendation. WWW, 2020.

Deldjoo et al. Adversarial Machine Learning in Recommender Systems. WSDM, 2020.

Fan et al. Graph Neural Networks for Recommendations. IJCAI, 2021.

Lei et al. Conversational Recommendation: Formulation, Methods, and Evaluation. Recsys, 2021.

Yu et al. Self-Supervised Learning in Recommender Systems. WWW, 2022.

Zhao et al. Automated Machine Learning for Recommendations: Fundamentals and Advances. WWW, 2022. Deepak et al. Recommender Problems for Web Application. ICML, 2011.

Bart et al. Explaining the user experience of recommender systems. Recsys, 2012.

Ester et al. Recommendation in Social Networks. Recsys, 2013.

Ivan et al. Cross-Domain Recommender Systems. Recsys, 2014.

Steck et al. Interactive Recommender Systems. Recsys, 2015.

Frank et al. Real-Time Recommendation of Streamed Data. Recsys, 2015.

Boratto et al. Group Recommender Systems. Recsys, 2016.

Alex et al. Deep Learning for Recommender Systems. Recsys, 2017.

Bart et al. -Privacy for Recommender Systems. Recsys, 2017.

Xu et al. Deep learning for matching in search and recommendation. SIGIR, 2018.

Massimo et al. Sequence-Aware Recommenders. Recsys, 2018.

Wang et al. Learning and Reasoning on Graph for Recommendation. CIKM, 2019.

Sonie et al. Concept to Code: Deep Learning for Multitask Recommendation. Recsys, 2019.

Michael at al. Fairness & Discrimination in Recommendation & Retrieval. Recsys, 2019.

Yang et al. Deep Transfer Learning for Search and Recommendation. WWW, 2020.

Deldjoo et al. Adversarial Machine Learning in Recommender Systems. WSDM, 2020.

Fan et al. Graph Neural Networks for Recommendations. IJCAI, 2021.

Lei et al. Conversational Recommendation: Formulation, Methods, and Evaluation. Recsys, 2021.

Yu et al. Self-Supervised Learning in Recommender Systems. WWW, 2022.

Zhao et al. Automated Machine Learning for Recommendations: Fundamentals and Advances. WWW, 2022.

综述

https://github.com/hongleizhang/RSPapers#surveys

Burke et al. Hybrid Recommender Systems: Survey and Experiments. USER MODEL USER-ADAP, 2002.

Adomavicius et al. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 2005.

Su et al. A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009.

Asela et al. A Survey of Accuracy Evaluation Metrics of Recommendation Tasks. J. Mach. Learn. Res, 2009.

Cacheda et al. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM TWEB, 2011.

Zhang et al. Tag-aware recommender systems: a state-of-the-art survey. J COMPUT SCI TECHNOL, 2011.

Tang et al. Social recommendation: a review. SNAM, 2013.

Yang et al. A survey of collaborative filtering based social recommender systems. COMPUT COMMUN, 2014.

Shi et al. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM COMPUT SURV, 2014.

Gunes et al. Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review, 2014.

Chen et al. Recommender systems based on user reviews: the state of the art. USER MODEL USER-ADAP, 2015.

Xu et al. Social networking meets recommender systems: survey. Int.J.Social Network Mining, 2015.

Yu et al. A survey of point-of-interest recommendation in location-based social networks. In Workshops at AAAI, 2015.

Efthalia et al. Parallel and Distributed Collaborative Filtering: A Survey. Comput. Surv., 2016.

Singhal et al. Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works. arXiv, 2017.

Muhammad et al. Cross Domain Recommender Systems: A Systematic Literature Review. ACM Comput. Surv, 2017.

Massimo et al. Sequence-Aware Recommender Systems. ACM Comput. Surv, 2018.

Zhang et al. Deep learning based recommender system: A survey and new perspectives. ACM Comput.Surv, 2018.

Batmaz et al. A review on deep learning for recommender systems: challenges and remedies. Artificial Intelligence Review, 2018.

Zhang et al. Explainable Recommendation: A Survey and New Perspectives. arXiv, 2018.

Liu et al. Survey of matrix factorization based recommendation methods by integrating social information. Journal of Software, 2018.

Shoujin et al. A Survey on Session-based Recommender Systems. arXiv, 2019.

Shoujin et al. Sequential Recommender Systems: Challenges, Progress and Prospects. IJCAI, 2019.

Zhu et al. Research Commentary on Recommendations with Side Information: A Survey and Research Directions. Electron. Commer. Res. Appl., 2019.

Lina et al. Recommendations on the Internet of Things: Requirements, Challenges, and Directions. IEEE Internet Comput., 2019.

Sriharsha et al. A Survey on Group Recommender Systems. J. Intell. Inf. Syst., 2019.

Dietmar et al. A Survey on Conversational Recommender Systems. arXiv, 2020.

Qingyu et al. A Survey on Knowledge Graph-Based Recommender Systems. arXiv, 2020.

Yang et al. Deep Learning on Knowledge Graph for Recommender System: A Survey. arXiv, 2020.

Wang et al. Graph Learning Approaches to Recommender Systems: A Review. arXiv, 2020.

Yashar et al. Adversarial Machine Learning in Recommender Systems-State of the art and Challenges. arXiv, 2020.

May et al. Recommender Systems for the Internet of Things: A Survey. arXiv, 2020.

Wu et al. Graph Neural Networks in Recommender Systems: A Survey. arXiv, 2020.

Chen et al. Bias and Debias in Recommender System: A Survey and Future Directions. arXiv, 2020.

Zhu et al. Cross-Domain Recommendation-Challenges, Progress, and Prospects. arxiv, 2021.

Zhang et al. Deep Learning for Click-Through Rate Estimation. IJCAI, 2021.

Wu et al. A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation. TKDE, 2021.

Chen et al. A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions. arxiv, 2021.

Lin et al. A Survey on Reinforcement Learning for Recommender Systems. arXiv, 2022.

Zheng et al. AutoML for Deep Recommender Systems: A Survey. arXiv, 2022.

Chen et al. Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation., arXiv, 2022.

Yu et al. Self-Supervised Learning for Recommender Systems: A Survey. arXiv, 2022.

Wang et al. A Survey on the Fairness of Recommender Systems.  TOIS, 2022.

Wang et al. Deep Meta-learning in Recommendation Systems: A Survey. arXiv, 2022.

视频

• RecSys Paper Presentation Videos (ACM RecSys) https://www.youtube.com/channel/UC2nEn-yNA1BtdDNWziphPGA/featured
• Building Recommender System with Machine Learning and AI (Youtube SEO) https://www.youtube.com/playlist?list=PLk9tco_9NSqfkr2Z0VdntKqufR5uDOezz
• Machine Learning - FULL COURSE | Andrew Ng | Stanford University (Lecture 16.1 ~ Lecture 16.6) https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN
• Mining Massive Datasets - FULL COURSE | Stanford University (Lecture 41 ~ Lecture 45) https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV
• Text Retrieval and Search Engines - FULL COURSE | UIUC (Lecture 38 ~ Lecture 42)   https://www.youtube.com/playlist?list=PLLssT5z_DsK8Jk8mpFc_RPzn2obhotfDO
• Recommendation Systems - Learn Python for Data Science #3 (Siraj Raval) https://www.youtube.com/watch?v=9gBC9R-msAk
• How does Netflix recommend movies? Matrix Factorization (Luis Serrano) https://www.youtube.com/watch?v=ZspR5PZemcs
• Machine Learning for Recommender Systems (James Kirk Spotify) https://www.youtube.com/watch?v=xBMGr08fowA&t=3m58s
• 工业界推荐系统介绍-Shusen Wang https://space.bilibili.com/1369507485/channel/seriesdetail?sid=2249610

论文笔记

深度剖析 | 因果强化学习在交互式推荐的前沿探索

快手+中科大 | 全曝光推荐数据集KuaiRec 2.0版本

WWW2022 | Recommendation Unlearning

SimpleX: 一个简单且有效的协同过滤框架

SIGIR2022 | 基于Prompt的用户自选公平性推荐算法

SIGIR2022 | UCCR: 以用户为中心的对话推荐系统

SIGIR2022 | SimGCL: 面向推荐系统的极简图对比学习方法

WWW2022 | 采用推荐系统打击虚假新闻

首篇自监督学习推荐系统综述: 150篇文献概述四大类方法(含开源算法库SELFRec)

TKDE2022 | 最新深度学习推荐系统综述：从协同过滤到信息增强的推荐系统

多视图多行为对比学习推荐系统

参考链接

• https://zhuanlan.zhihu.com/p/34004488
• https://github.com/jihoo-kim/awesome-RecSys

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深度剖析 | 因果强化学习在交互式推荐的前沿探索

快手推荐算法工程师工作感悟

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