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Adaptive Graph Contrastive Learning for Recommendation

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Lianghao HuangAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 4252 - 4261

https://doi.org/10.1145/3580305.3599768

Published: 04 August 2023 Publication History

Abstract

Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is to recursively perform message passing along user-item interaction edges to refine encoded embeddings, relying on sufficient and high-quality training data. However, user behavior data in practical recommendation scenarios is often noisy and exhibits skewed distribution. To address these issues, some recommendation approaches, such as SGL, leverage self-supervised learning to improve user representations. These approaches conduct self-supervised learning through creating contrastive views, but they depend on the tedious trial-and-error selection of augmentation methods. In this paper, we propose a novel Adaptive Graph Contrastive Learning (AdaGCL) framework that conducts data augmentation with two adaptive contrastive view generators to better empower the CF paradigm. Specifically, we use two trainable view generators - a graph generative model and a graph denoising model - to create adaptive contrastive views. With two adaptive contrastive views, AdaGCL introduces additional high-quality training signals into the CF paradigm, helping to alleviate data sparsity and noise issues. Extensive experiments on three real-world datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods. Our model implementation codes are available at the link https://github.com/HKUDS/AdaGCL.

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Cited By

Wei TYang CZheng Y(2025)Prototypical Graph Contrastive Learning for RecommendationApplied Sciences10.3390/app1504196115:4(1961)Online publication date: 13-Feb-2025
https://doi.org/10.3390/app15041961
Peng YGao CZhang YDan TDu XLuo HLi YMeng X(2025)Denoising Alignment with Large Language Model for RecommendationACM Transactions on Information Systems10.1145/369666243:2(1-35)Online publication date: 24-Jan-2025
https://dl.acm.org/doi/10.1145/3696662
Qin ZCheng WDing WDing G(2025)Hyperbolic Graph Contrastive Learning for Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352296037:3(1255-1267)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3522960
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Index Terms

Adaptive 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

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

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Published: 04 August 2023

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Cited By

Wei TYang CZheng Y(2025)Prototypical Graph Contrastive Learning for RecommendationApplied Sciences10.3390/app1504196115:4(1961)Online publication date: 13-Feb-2025
https://doi.org/10.3390/app15041961
Peng YGao CZhang YDan TDu XLuo HLi YMeng X(2025)Denoising Alignment with Large Language Model for RecommendationACM Transactions on Information Systems10.1145/369666243:2(1-35)Online publication date: 24-Jan-2025
https://dl.acm.org/doi/10.1145/3696662
Qin ZCheng WDing WDing G(2025)Hyperbolic Graph Contrastive Learning for Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352296037:3(1255-1267)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3522960
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
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
Mo XZhao ZHe XQi HLiu H(2025)Intelligible graph contrastive learning with attention-aware for recommendationNeurocomputing10.1016/j.neucom.2024.128781614:COnline publication date: 21-Jan-2025
https://dl.acm.org/doi/10.1016/j.neucom.2024.128781
Li XHuang ZWu ZWang CChen Y(2025)Cross-domain recommendation via knowledge distillationKnowledge-Based Systems10.1016/j.knosys.2025.113112311(113112)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.113112
Gunathilaka TManage PZhang JLi YKelly W(2025)Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniquesIntelligent Systems with Applications10.1016/j.iswa.2024.20047425(200474)Online publication date: Mar-2025
https://doi.org/10.1016/j.iswa.2024.200474
Wang XYue HGuo LGuo FHe CHan X(2025)User identification network with contrastive clustering for shared-account recommendationInformation Processing & Management10.1016/j.ipm.2024.10405562:3(104055)Online publication date: May-2025
https://doi.org/10.1016/j.ipm.2024.104055
Yang YGuan SWen X(2025)Enhancing robustness in implicit feedback recommender systems with subgraph contrastive learningInformation Processing & Management10.1016/j.ipm.2024.10396262:1(103962)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103962
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