RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation

TL;DR

RaDAR is a novel graph contrastive learning framework for recommendation that enhances robustness and accuracy by using relation-aware diffusion, asymmetric contrastive learning, and edge refinement, especially effective in noisy and sparse data scenarios.

Contribution

RaDAR introduces a relation-aware, diffusion-guided augmentation framework with asymmetric contrastive learning and dynamic edge refinement for improved recommendation accuracy.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Demonstrates robustness under noisy and sparse data conditions

Effectively refines graph structure for better semantic alignment

Abstract

Collaborative filtering (CF) recommendation has been significantly advanced by integrating Graph Neural Networks (GNNs) and Graph Contrastive Learning (GCL). However, (i) random edge perturbations often distort critical structural signals and degrade semantic consistency across augmented views, and (ii) data sparsity hampers the propagation of collaborative signals, limiting generalization. To tackle these challenges, we propose RaDAR (Relation-aware Diffusion-Asymmetric Graph Contrastive Learning Framework for Recommendation Systems), a novel framework that combines two complementary view generation mechanisms: a graph generative model to capture global structure and a relation-aware denoising model to refine noisy edges. RaDAR introduces three key innovations: (1) asymmetric contrastive learning with global negative sampling to maintain semantic alignment while suppressing noise; (2) diffusion-guided augmentation, which employs progressive noise injection and denoising for enhanced robustness; and (3) relation-aware edge refinement, dynamically adjusting edge weights based on latent node semantics. Extensive experiments on three public benchmarks demonstrate that RaDAR consistently outperforms state-of-the-art methods, particularly under noisy and sparse conditions.