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Must-read papers on Recommender System

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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 and privacy preserving recommender system.

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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.

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

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

Tutorials

Surveys

General Recommender System

Social Recommender System

Deep Learning based Recommender System

Cold Start Problem in Recommender System

POI Recommender System

Efficient RS

EE in RS

Explainability on RS

CTR Prediction for RS

Knowledge Graph for RS

Review based RS

Conversational RS

Industrial RS

[Adobe]

[Airbnb]

[Alibaba]

[Baidu]

[ByteDance]

[Criteo]

[Facebook]

[Google]

[Huawei]

[JingDong]

[Meituan]

[Microsoft]

[Netflix]

[Pinterest]

[Sina]

[Tencent]

[X (Twitter)]

[Yahoo]

Privacy in RS

[Federated Learning]

LLM for RS

Star History

Star History Chart

RSAlgorithms

Recently, we have launched an open source project RSAlgorithms, which provides an integrated training and testing framework. In this framework, we implement a set of classical traditional recommendation methods which make predictions only using rating data and social recommendation methods which utilize trust/social information in order to alleviate the sparsity of ratings data. Besides, we have collected some classical methods implemented by others for your convenience.

Acknowledgements

Specially summerize the papers about Recommender Systems for you, and if you have any questions, please contact me generously. Last but not least, the ability of myself is limited so I sincerely look forward to working with you to contribute it.

Thank @ShawnSu for collecting papers about POI Recommender Systems.

Thank @Wang Zhe for his advice about EE in RS.

Highly thank @Yujia Zhang for her summary on Hashing for RS.

Thank @Zixuan Yang and @vicki1109 for his collecting papers about CTR Prediction for RS.

Thank @ShomyLiu for collecting papers about Review based RS.

Thank @Fanshaoliu for collecting papers about Industrial RS.