research-article

Multi-Relational Contrastive Learning for Recommendation

Authors:

Chao HuangAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 338 - 349

https://doi.org/10.1145/3604915.3608807

Published: 14 September 2023 Publication History

Abstract

Personalized recommender systems play a crucial role in capturing users’ evolving preferences over time to provide accurate and effective recommendations on various online platforms. However, many recommendation models rely on a single type of behavior learning, which limits their ability to represent the complex relationships between users and items in real-life scenarios. In such situations, users interact with items in multiple ways, including clicking, tagging as favorite, reviewing, and purchasing. To address this issue, we propose the Relation-aware Contrastive Learning (RCL) framework, which effectively models dynamic interaction heterogeneity. The RCL model incorporates a multi-relational graph encoder that captures short-term preference heterogeneity while preserving the dedicated relation semantics for different types of user-item interactions. Moreover, we design a dynamic cross-relational memory network that enables the RCL model to capture users’ long-term multi-behavior preferences and the underlying evolving cross-type behavior dependencies over time. To obtain robust and informative user representations with both commonality and diversity across multi-behavior interactions, we introduce a multi-relational contrastive learning paradigm with heterogeneous short- and long-term interest modeling. Our extensive experimental studies on several real-world datasets demonstrate the superiority of the RCL recommender system over various state-of-the-art baselines in terms of recommendation accuracy and effectiveness. We provide the implementation codes for the RCL model at https://github.com/HKUDS/RCL.

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Cited By

Liu ZWang CZheng SWu CZheng KSong YMou N(2025)Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender SystemsACM Transactions on Recommender Systems10.1145/3711666Online publication date: 4-Jan-2025
https://dl.acm.org/doi/10.1145/3711666
Zhang SChu HLi JZhou YWang SSun QNejdl WAuer SCha MMoens MNajork M(2025)DeMBR: Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703532(521-529)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703532
Mo XPang JWan BTang RLiu HJiang S(2025)Multi-relational graph contrastive learning with learnable graph augmentationNeural Networks10.1016/j.neunet.2024.106757181(106757)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106757
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Index Terms

Multi-Relational Contrastive Learning for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
2. Security and privacy
  1. Software and application security

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 ACM.

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Published: 14 September 2023

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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Cited By

Liu ZWang CZheng SWu CZheng KSong YMou N(2025)Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender SystemsACM Transactions on Recommender Systems10.1145/3711666Online publication date: 4-Jan-2025
https://dl.acm.org/doi/10.1145/3711666
Zhang SChu HLi JZhou YWang SSun QNejdl WAuer SCha MMoens MNajork M(2025)DeMBR: Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703532(521-529)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703532
Mo XPang JWan BTang RLiu HJiang S(2025)Multi-relational graph contrastive learning with learnable graph augmentationNeural Networks10.1016/j.neunet.2024.106757181(106757)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106757
Lan WZhou GChen QWang WPan SPan YZhang S(2024)Contrastive Clustering Learning for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/369819243:1(1-23)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3698192
Wei WRen XTang JWang QSu LCheng SWang JYin DHuang CAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)LLMRec: Large Language Models with Graph Augmentation for RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635853(806-815)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635853
Zhao ZTong XWang YZhang Q(2024)Multi-behavior contrastive learning with graph neural networks for recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112221300(112221)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112221
Gu PHu HXu G(2024)Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111841295:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111841
Xiangyi YChang MHengyu ZYongsheng L(2024)Hypergraph projection enhanced collaborative filteringInternational Journal of Data Science and Analytics10.1007/s41060-024-00508-x19:2(269-281)Online publication date: 6-Feb-2024
https://doi.org/10.1007/s41060-024-00508-x

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