提出新推荐框架，提升稀疏和噪声数据下的推荐效果
RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation
- 用生成模型捕捉全局结构，用关系感知去噪模型优化边
- 在三个公开数据集上超越现有方法，尤其在稀疏场景下表现突出
- 适合处理数据稀疏、存在噪声的推荐系统场景
协同过滤推荐已通过引入图神经网络（GNN）和图对比学习（GCL）取得显著进展。然而，(i) 随机边扰动常扭曲关键结构信号，降低增强视图间的语义一致性；(ii) 数据稀疏性阻碍协同信号传播，限制泛化能力。为此，我们提出RaDAR（关系感知扩散-非对称图对比学习推荐框架），结合两种互补的视图生成机制：图生成模型用于捕捉全局结构，关系感知去噪模型用于精炼噪声边。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，保持语义对齐同时抑制噪声；(2) 扩散引导增强，通过逐步注入噪声与去噪提升鲁棒性；(3) 关系感知边优化，根据节点潜在语义动态调整边权重。在三个公开基准上的大量实验表明，RaDAR持续优于现有先进方法，尤其在噪声和稀疏条件下表现更优。
原文摘要 · Abstract (English)
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.
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