research-article

Heterogeneous Graph Contrastive Learning for Recommendation

Authors:

Ronghua LuoAuthors Info & Claims

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

Pages 544 - 552

https://doi.org/10.1145/3539597.3570484

Published: 27 February 2023 Publication History

Abstract

Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance the user preference learning. In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation. Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a <u>H</u>eterogeneous <u>G</u>raph <u>C</u>ontrastive <u>L</u>earning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views. However, the influence of heterogeneous side information on interactions may vary by users and items. To move this idea forward, we enhance our heterogeneous graph contrastive learning with meta networks to allow the personalized knowledge transformer with adaptive contrastive augmentation. The experimental results on three real-world datasets demonstrate the superiority of HGCL over state-of-the-art recommendation methods. Through ablation study, key components in HGCL method are validated to benefit the recommendation performance improvement. The source code of the model implementation is available at the link https://github.com/HKUDS/HGCL.

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Cited By

LI XLI YZHANG JXU XLIU D(2024)Agent Allocation-Action Learning with Dynamic Heterogeneous Graph in Multi-Task GamesIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7180E107.D:8(1040-1049)Online publication date: 1-Aug-2024
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Yang ZZhang GWu JYang JSheng QXue SZhou CAggarwal CPeng HHu WHancock ELiò P(2024)State of the Art and Potentialities of Graph-level LearningACM Computing Surveys10.1145/3695863Online publication date: 12-Sep-2024
https://dl.acm.org/doi/10.1145/3695863
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Index Terms

Heterogeneous Graph Contrastive Learning for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

February 2023

1345 pages

ISBN:9781450394079

DOI:10.1145/3539597

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Hady Lauw
Singapore Management University
,
Program Chairs:
Luo Si
Salesforce
,
Evimaria Terzi
Boston University
,
Panayiotis Tsaparas
University of Ioannina

Copyright © 2023 ACM.

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Published: 27 February 2023

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February 27 - March 3, 2023

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Cited By

LI XLI YZHANG JXU XLIU D(2024)Agent Allocation-Action Learning with Dynamic Heterogeneous Graph in Multi-Task GamesIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7180E107.D:8(1040-1049)Online publication date: 1-Aug-2024
https://doi.org/10.1587/transinf.2023EDP7180
Yang ZZhang GWu JYang JSheng QXue SZhou CAggarwal CPeng HHu WHancock ELiò P(2024)State of the Art and Potentialities of Graph-level LearningACM Computing Surveys10.1145/3695863Online publication date: 12-Sep-2024
https://dl.acm.org/doi/10.1145/3695863
Zhao ZBai YXiong RCao QMa CJiang NWu FKuang K(2024)Learning Individual Treatment Effects under Heterogeneous Interference in NetworksACM Transactions on Knowledge Discovery from Data10.1145/367376118:8(1-21)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3673761
Li MLiu KLiu HZhao ZWard TWu X(2024)Heterogeneous Meta-Path Graph Learning for Higher-Order Social RecommendationACM Transactions on Knowledge Discovery from Data10.1145/367365818:8(1-25)Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1145/3673658
Cheng ZDong JLiu FZhu LYang XWang M(2024)Disentangled Cascaded Graph Convolution Networks for Multi-Behavior RecommendationACM Transactions on Recommender Systems10.1145/36732442:4(1-27)Online publication date: 17-Jun-2024
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Sun YSun ZDu YZhang JOng YBaeza-Yates RBonchi F(2024)Self-Supervised Denoising through Independent Cascade Graph Augmentation for Robust Social RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671958(2806-2817)Online publication date: 25-Aug-2024
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Duan HXie CLi LBaeza-Yates RBonchi F(2024)Reserving-Masking-Reconstruction Model for Self-Supervised Heterogeneous Graph RepresentationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671719(689-700)Online publication date: 25-Aug-2024
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Li HCheng ZYu XLiu JLiu GDu JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Behavior Pattern Mining-based Multi-Behavior RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657973(2291-2295)Online publication date: 10-Jul-2024
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Lai YSu YWei LHe TWang HChen GZha DLiu QWang XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Disentangled Contrastive Hypergraph Learning for Next POI RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657726(1452-1462)Online publication date: 10-Jul-2024
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Jiang YYang YXia LHuang CAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)DiffKG: Knowledge Graph Diffusion Model for RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635850(313-321)Online publication date: 4-Mar-2024
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