Must-read papers on Recommender System

This repository provides a curated list of papers and tutorials about Recommender Systems (RS) including systematic tutorials, comprehensive surveys, general recommender system, social recommender system, deep learing-based recommender system, cold start problem in recommender system, efficient recommender system, exploration and exploitation problem in recommender system, explainability in recommender system as well as click through rate prediction for recommender system, knowledge graph for recommeder system, review based recommender system, conversational recommender system, industrial/practical recommender system, privacy preserving recommender system, large language models for recommender system and agentic recommender system.

• [New!] Add the new part of Agentic RS.

• [New!] Add the new part of LLM for RS.

• [New!] Add the new part of RS-Tutorials.

• [New!] Add the new part of Privacy&Security RS.

===========================================================================

00-Tutorials: contain so many tutorials on recommendation systems given by prominent researchers at many top-tier conferences

01-Surveys: a set of comprehensive surveys about recommender system, such as hybrid recommender systems, social recommender systems, poi recommender systems, deep-learning based recommonder systems and so on.

02-General RS: a set of famous recommendation papers which make predictions with some classic models and practical theory.

03-Social RS: several papers which utilize trust/social information in order to alleviate the sparsity of ratings data.

04-Deep Learning-based RS: a set of papers to build a recommender system with deep learning techniques.

05-Cold Start Problem in RS: some papers specifically dealing with the cold start problems inherent in collaborative filtering.

06-POI RS: it focus on helping users explore attractive locations with the information of location-based social networks.

07-Efficient RS: some techniques for efficient recommender system in order to training and making recommendation efficiently.

08-EE Problem in RS: some articles about exploration and exploitation problems in recommendation.

09-Explainability on RS: it focus on addressing the problem of 'why', they not only provide the user with the recommendations, but also make the user aware why such items are recommended by generating recommendation explanations.

10-CTR Prediction for RS: as one part of recommendation, click-through rate prediction focuses on the elaboration of candidate sets for recommendation.

11-Knowledge Graph for RS: knowledge graph, as the side information of behavior interaction matrix in recent years, which can effectively alleviate the problem of data sparsity and cold start, and can provide a reliable explanation for recommendation results.

12-Review based RS: some articles about review or text based recommendations.

13-Conversational RS: some papers made use of natural language processing technology to interactively provide recommendations.

14-Industrial RS: some papers on best practices published in industry.

15-Privacy&Security RS: some papers about privacy preserving and security in recommder systems.

16-LLM for RS: some papers about large language models in recommder systems.

17-Agentic RS: some papers about LLM-based agents for recommender systems.

===========================================================================

* Please help to contribute this list by adding pull request with the template below.

* Author Name et al. **Paper Name.**  Conference/Journal, Year.

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.

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. Re