tutorial

Self-Supervised Learning for Recommender System

Authors:

Dawei YinAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3440 - 3443

https://doi.org/10.1145/3477495.3532684

Published: 07 July 2022 Publication History

Abstract

Recommender systems have become key components for a wide spectrum of web applications (e.g., E-commerce sites, video sharing platforms, lifestyle applications, etc), so as to alleviate the information overload and suggest items for users. However, most existing recommendation models follow a supervised learning manner, which notably limits their representation ability with the ubiquitous sparse and noisy data in practical applications. Recently, self-supervised learning (SSL) has become a promising learning paradigm to distill informative knowledge from unlabeled data, without the heavy reliance on sufficient supervision signals. Inspired by the effectiveness of self-supervised learning, recent efforts bring SSL's superiority into various recommendation representation learning scenarios with augmented auxiliary learning tasks. In this tutorial, we aim to provide a systemic review of existing self-supervised learning frameworks and analyze the corresponding challenges for various recommendation scenarios, such as general collaborative filtering paradigm, social recommendation, sequential recommendation, and multi-behavior recommendation. We then raise discussions and future directions of this area. With the introduction of this emerging and promising topic, we expect the audience to have a deep understanding of this domain. We also seek to promote more ideas and discussions, which facilitates the development of self-supervised learning recommendation techniques.

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Cited By

Zhou JLiao JZhu XWen JZhou W(2025)Simplified self-supervised learning for hybrid propagation graph-based recommendationNeural Networks10.1016/j.neunet.2025.107145185(107145)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107145
Abdulrazzaq MRamaha NHameed ASalman MYon DFitriyani NSyafrudin MLee S(2024)Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning ContextsMathematics10.3390/math1205075812:5(758)Online publication date: 3-Mar-2024
https://doi.org/10.3390/math12050758
Zha RSun YQin CZhang LXu TZhu HChen E(2024)Towards Unified Representation Learning for Career Mobility Analysis with Trajectory HypergraphACM Transactions on Information Systems10.1145/365115842:4(1-28)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3651158
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Index Terms

Self-Supervised Learning for Recommender System
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Zhou JLiao JZhu XWen JZhou W(2025)Simplified self-supervised learning for hybrid propagation graph-based recommendationNeural Networks10.1016/j.neunet.2025.107145185(107145)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107145
Abdulrazzaq MRamaha NHameed ASalman MYon DFitriyani NSyafrudin MLee S(2024)Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning ContextsMathematics10.3390/math1205075812:5(758)Online publication date: 3-Mar-2024
https://doi.org/10.3390/math12050758
Zha RSun YQin CZhang LXu TZhu HChen E(2024)Towards Unified Representation Learning for Career Mobility Analysis with Trajectory HypergraphACM Transactions on Information Systems10.1145/365115842:4(1-28)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3651158
Gao QHong JXu XKuang PZhou FTrajcevski G(2024)Predicting Human Mobility via Self-Supervised Disentanglement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331717536:5(2126-2141)Online publication date: May-2024
https://doi.org/10.1109/TKDE.2023.3317175
Chen CTian HZhang HDou W(2024)A Contrastive Collaborative Filtering Method for Personalized Recommendation with Self-Attention2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00015(50-57)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00015
Zhang HLi W(2024)LSMRec: Leveraging Hash-Enhanced Semantic Mapping for Superior Sequential Recommendations2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI62512.2024.00032(166-173)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ICTAI62512.2024.00032
Shang JJiao YGuo CSun MGao YLiu JMomma MTaru ISun Y(2024)Transitivity-Encoded Graph Attention Networks for Complementary Item Recommendations2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00050(430-439)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00050
Zhu XLi LLiu WLuo X(2024)Multi-level sequence denoising with cross-signal contrastive learning for sequential recommendationNeural Networks10.1016/j.neunet.2024.106480179(106480)Online publication date: Nov-2024
https://doi.org/10.1016/j.neunet.2024.106480
Jiang SZhao C(2023)A cascaded embedding method with graph neural network for multi-behavior recommendationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-02045-815:6(2513-2526)Online publication date: 13-Dec-2023
https://doi.org/10.1007/s13042-023-02045-8
Li QMa HZhang RJin WLi Z(2023)Dual-view co-contrastive learning for multi-behavior recommendationApplied Intelligence10.1007/s10489-023-04495-753:17(20134-20151)Online publication date: 30-Mar-2023
https://dl.acm.org/doi/10.1007/s10489-023-04495-7
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