research-article

Contrastive Self-supervised Learning in Recommender Systems: A Survey

Authors:

Ke WangAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 2

Article No.: 59, Pages 1 - 39

https://doi.org/10.1145/3627158

Published: 08 November 2023 Publication History

Abstract

Deep learning-based recommender systems have achieved remarkable success in recent years. However, these methods usually heavily rely on labeled data (i.e., user-item interactions), suffering from problems such as data sparsity and cold-start. Self-supervised learning, an emerging paradigm that extracts information from unlabeled data, provides insights into addressing these problems. Specifically, contrastive self-supervised learning, due to its flexibility and promising performance, has attracted considerable interest and recently become a dominant branch in self-supervised learning-based recommendation methods. In this survey, we provide an up-to-date and comprehensive review of current contrastive self-supervised learning-based recommendation methods. Firstly, we propose a unified framework for these methods. We then introduce a taxonomy based on the key components of the framework, including view generation strategy, contrastive task, and contrastive objective. For each component, we provide detailed descriptions and discussions to guide the choice of the appropriate method. Finally, we outline open issues and promising directions for future research.

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Index Terms

Contrastive Self-supervised Learning in Recommender Systems: A Survey
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 2

March 2024

897 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3618075

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 08 November 2023

Online AM: 09 October 2023

Accepted: 29 September 2023

Revised: 05 August 2023

Received: 17 March 2023

Published in TOIS Volume 42, Issue 2

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Cited By

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https://dl.acm.org/doi/10.1145/3653673
Liu YXia LHuang CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)SelfGNN: Self-Supervised Graph Neural Networks for Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657716(1609-1618)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657716
Wang NLiu DZeng JMu LLi J(2024)HGRec: Group Recommendation With Hypergraph Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.336384311:3(4214-4225)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2024.3363843
Xia TRen JRao WZu QWang WChen SZhang Y(2024)AeroRec: An Efficient On-Device Recommendation Framework using Federated Self-Supervised Knowledge DistillationIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621240(121-130)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621240
Xia BQin JHan LGao AMa C(2024)Knowledge filter contrastive learning for recommendationKnowledge and Information Systems10.1007/s10115-024-02158-8Online publication date: 16-Jul-2024
https://doi.org/10.1007/s10115-024-02158-8
Wang JZhu YJing MZang TChen Q(2023)Dual Contrastive Learning for Multi-Behavior Recommendation2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00217(1538-1545)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ICPADS60453.2023.00217
Liu DWang NLi J(2023)Cross-view Self-Supervised Learning with Graph Convolutional Networks for Group Recommendation2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00216(1530-1537)Online publication date: 17-Dec-2023
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Shi JZhu YWang KJing MZang TYu JTang F(2023)Rating-Review Graph Contrastive Learning for Review-based Recommendation2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00215(1522-1529)Online publication date: 17-Dec-2023
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Choi JRyu D(2023)QoS-Aware Graph Contrastive Learning for Web Service Recommendation2023 30th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC60848.2023.00027(171-180)Online publication date: 4-Dec-2023
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