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

arXiv cs.LG / 3/18/2026

📰 NewsModels & Research

共有:

Key Points

RaDAR introduces a two-branch framework that marries a graph generative model capturing global structure with a relation-aware denoising component to refine noisy connections in recommendation graphs.
It proposes asymmetric contrastive learning with global negative sampling to preserve semantic alignment while suppressing noise.
It uses diffusion-guided augmentation, applying progressive noise injection and denoising to boost robustness under data sparsity and noise.
It includes relation-aware edge refinement that dynamically adjusts edge weights based on latent node semantics, with experiments on three public benchmarks showing consistent gains over state-of-the-art methods in noisy and sparse conditions.

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.