High-Order Fusion Graph Contrastive Learning for Recommendation

• URL: http://arxiv.org/abs/2407.19692v2
• Date: Sun, 29 Sep 2024 10:12:40 GMT
• Title: High-Order Fusion Graph Contrastive Learning for Recommendation
• Authors: Yu Zhang, Lei Sang, Yi Zhang, Yiwen Zhang, Yun Yang,
• Abstract summary: Graph contrastive learning (GCL)-based methods typically implement CL by creating contrastive views through various data augmentation techniques. Existing CL-based methods use traditional CL objectives to capture self-supervised signals. We propose a High-order Fusion Graph Contrastive Learning (HFGCL) framework for recommendation.
• Score: 16.02820746003461
• License:
• Abstract: Self-supervised learning (SSL) has recently attracted significant attention in the field of recommender systems. Contrastive learning (CL) stands out as a major SSL paradigm due to its robust ability to generate self-supervised signals. Mainstream graph contrastive learning (GCL)-based methods typically implement CL by creating contrastive views through various data augmentation techniques. Despite these methods are effective, we argue that there still exist several challenges. i) Data augmentation (e.g., discarding edges or adding noise) necessitates additional graph convolution (GCN) or modeling operations, which are highly time-consuming and potentially harm the embedding quality. ii) Existing CL-based methods use traditional CL objectives to capture self-supervised signals. However, few studies have explored obtaining CL objectives from more perspectives and have attempted to fuse the varying signals from these CL objectives to enhance recommendation performance. To overcome these challenges, we propose a High-order Fusion Graph Contrastive Learning (HFGCL) framework for recommendation. Specifically, instead of facilitating data augmentations, we use high-order information from GCN process to create contrastive views. Additionally, to integrate self-supervised signals from various CL objectives, we propose an advanced CL objective. By ensuring that positive pairs are distanced from negative samples derived from both contrastive views, we effectively fuse self-supervised signals from distinct CL objectives, thereby enhancing the mutual information between positive pairs. Experimental results on three public datasets demonstrate the superior recommendation performance and efficiency of HFGCL compared to the state-of-the-art baselines.

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