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Self-Supervised Learning for Recommendation

Published: 17 October 2022 Publication History

Abstract

Recommender systems are playing an increasingly critical role to alleviate information overload and satisfy users' information seeking requirements in a wide spectrum of online platforms. However, the ubiquity of data sparsity and noise notably limits the representation capacity of existing recommender systems to learn high-quality user (item) embeddings. Inspired by recent advances of self-supervised learning (SSL) techniques, SSL-based representation learning models benefit a variety of recommendation domains. Such methods have achieved new levels of performance while reducing the dependence on observed supervision labels in diverse recommendation tasks. In this tutorial, we aim to provide a systemic review of state-of-the-art SSL-based recommender systems. To be specific, we summarize and categorize existing work of SSL-based recommender systems in terms of recommendation scenarios. For each type of recommendation task, the corresponding challenges and methods will be presented in a comprehensive way. Finally, some future directions and open questions will be raised to inspire more investigation on this important research line.

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Cited By

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  • (2024)Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive LearningMathematics10.3390/math1213205712:13(2057)Online publication date: 30-Jun-2024
  • (2024)Formalizing Multimedia Recommendation through Multimodal Deep LearningACM Transactions on Recommender Systems10.1145/3662738Online publication date: 29-Apr-2024
  • (2024)Data Augmentation for Conversational AICompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3641238(1234-1237)Online publication date: 13-May-2024
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cover image ACM Conferences
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
5274 pages
ISBN:9781450392365
DOI:10.1145/3511808
  • General Chairs:
  • Mohammad Al Hasan,
  • Li Xiong
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 October 2022

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Author Tags

  1. collaborative filtering
  2. contrastive learning
  3. graph neural networks
  4. recommender system
  5. self-supervised learning

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2024)Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive LearningMathematics10.3390/math1213205712:13(2057)Online publication date: 30-Jun-2024
  • (2024)Formalizing Multimedia Recommendation through Multimodal Deep LearningACM Transactions on Recommender Systems10.1145/3662738Online publication date: 29-Apr-2024
  • (2024)Data Augmentation for Conversational AICompanion Proceedings of the ACM on Web Conference 202410.1145/3589335.3641238(1234-1237)Online publication date: 13-May-2024
  • (2024)CGG: Category-aware global graph contrastive learning for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112661305(112661)Online publication date: Dec-2024
  • (2024)Causal intervention for knowledge graph denoising in recommender systemsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02500-0Online publication date: 21-Dec-2024
  • (2023)Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven AnalysisProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609489(350-361)Online publication date: 14-Sep-2023
  • (2023)SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615063(1566-1575)Online publication date: 21-Oct-2023
  • (2023)Complex Item Set RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3594248(3444-3447)Online publication date: 19-Jul-2023
  • (2023)Disentangled Hypergraph Collaborative Filtering for Social Recommendation2023 IEEE International Conference on Web Services (ICWS)10.1109/ICWS60048.2023.00066(475-482)Online publication date: Jul-2023

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