DiffGCL: Diffusion model-based Graph Contrastive Learning for Service Recommendation

Xiuqi Yang, Xiaojun Xu, Zhuofan Xu, Jing Chu, Jingjing Hu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recent years have witnessed the notable resurgence of graph neural networks (GNNs) in service recommender systems. GNN-based service recommender systems heavily rely on the recursive message propagation mechanism between layers. However, they encounter challenges due to data sparsity and susceptibility to noisy interactions, leading to unstable and suboptimal performance. Several studies have attempted to mitigate these issues by integrating GNNs with contrastive learning techniques. Despite their success, most existing approaches utilize either stochastic or heuristic data augmentation methods. We argue that these methods suffer from inherent limitations, including disruption of the original structure of the user-service interaction graph and the need for laborious and iterative experimentation. In this paper, we propose a novel Diffusion model-based Graph Contrastive Learning framework, named DiffGCL, for service recommendation. Specifically, we incorporate Diffusion models into service recommender systems as generators for contrastive learning views. We also introduce a self-adaptive data augmentation strategy that dynamically constructs new dual views each epoch while preserving the intrinsic semantic structure of the generated graph. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of DiffGCL, outperforming various baseline models on service recommendation tasks.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE International Conference on Web Services, ICWS 2024
EditorsRong N. Chang, Carl K. Chang, Zigui Jiang, Jingwei Yang, Zhi Jin, Michael Sheng, Jing Fan, Kenneth K. Fletcher, Qiang He, Qiang He, Claudio Ardagna, Jian Yang, Jianwei Yin, Zhongjie Wang, Amin Beheshti, Stefano Russo, Nimanthi Atukorala, Jia Wu, Philip S. Yu, Heiko Ludwig, Stephan Reiff-Marganiec, Emma Zhang, Anca Sailer, Nicola Bena, Kuang Li, Yuji Watanabe, Tiancheng Zhao, Shangguang Wang, Zhiying Tu, Yingjie Wang, Kang Wei
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages310-319
Number of pages10
ISBN (Electronic)9798350368550
DOIs
Publication statusPublished - 2024
Event2024 IEEE International Conference on Web Services, ICWS 2024 - Shenzhen, China
Duration: 7 Jul 202413 Jul 2024

Conference

Conference2024 IEEE International Conference on Web Services, ICWS 2024
Country/TerritoryChina
CityShenzhen
Period7/07/2413/07/24

Keywords

  • Collaborative Filtering
  • Contrastive Learning
  • Diffusion Models
  • Graph Neural Networks
  • Service Recommendation

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