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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 augmentations. Despite these methods are effective, we argue that there still exist several challenges. i) Data augmentation requires additional graph convolution (GCN) or modeling operations, significantly increasing time costs. Moreover, graph augmentation disrupts the intrinsic properties of the user-item graph by randomly removing nodes/edges, while feature augmentation applies noise to all nodes, neglecting their unique characteristics. ii) Existing GCL-based methods use traditional CL objectives to capture self-supervised signals. However, few studies have explored obtaining more beneficial CL objectives from more perspectives and have attempted to fuse the varying self-supervised signals from these CL objectives to enhance recommendation performance.
To overcome these challenges, we propose Adaptive Fusion Graph Contrastive Learning (AFGCL) for recommendation. AFGCL exploits structural information naturally produced during graph propagation to construct contrastive representations. Specifically, we introduce an adaptive fusion strategy that estimates the contributions of different propagation depths to the primary recommendation task and adaptively combines their representations. Furthermore, we construct an explicit representation for each observed user--item interaction and propose a fused contrastive objective. Experimental results on three public datasets demonstrate the superior recommendation performance and training efficiency of AFGCL compared with state-of-the-art baselines.

Submission history

From: Yu Zhang [view email]
[v1] Mon, 29 Jul 2024 04:30:38 UTC (2,271 KB)
[v2] Sun, 29 Sep 2024 10:12:40 UTC (2,575 KB)
[v3] Wed, 15 Jan 2025 05:04:59 UTC (2,104 KB)
[v4] Thu, 16 Jan 2025 02:17:21 UTC (2,104 KB)
[v5] Tue, 14 Jul 2026 06:03:23 UTC (2,762 KB)