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.

翻译：协同过滤（CF）推荐通过整合图神经网络（GNNs）和图对比学习（GCL）取得了显著进展。然而，（i）随机边扰动往往会扭曲关键的结构信号并破坏增强视图间的语义一致性；（ii）数据稀疏性阻碍了协同信号的传播，限制了模型的泛化能力。为应对这些挑战，我们提出RaDAR（面向推荐系统的关系感知扩散-非对称图对比学习框架），该新颖框架结合了两种互补的视图生成机制：捕捉全局结构的图生成模型与精修噪声边的关系感知去噪模型。RaDAR引入三项关键创新：（1）采用全局负采样的非对称对比学习，在抑制噪声的同时保持语义对齐；（2）扩散引导的增强机制，通过渐进噪声注入与去噪提升模型鲁棒性；（3）关系感知边优化，基于潜在节点语义动态调整边权重。在三个公开基准数据集上的大量实验表明，RaDAR始终优于现有最优方法，尤其在噪声与稀疏条件下表现突出。