用SVD增强图结构，提升推荐系统在稀疏数据下的表现
Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
- 通过SVD实现稳定图增强，避免随机扰动
- 在多个基准数据集上超越现有模型性能
- 有效缓解热门物品偏差，适合真实推荐场景
图神经网络在推荐系统中表现强大，但在数据稀疏和噪声环境下常表现不佳。为此，我们提出LightGCL，一种基于奇异值分解（SVD）的图对比学习模型，通过稳健的图增强策略，在不依赖随机或启发式扰动的前提下，保持语义一致性。该方法实现了结构优化，并捕获全局协同信号，在多个基准数据集上显著优于当前最优模型。实验还表明，其具备更好的公平性和对流行度偏见的鲁棒性，适用于真实推荐系统。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) are powerful tools for recommendation systems, but they often struggle under data sparsity and noise. To address these issues, we implemented LightGCL, a graph contrastive learning model that uses Singular Value Decomposition (SVD) for robust graph augmentation, preserving semantic integrity without relying on stochastic or heuristic perturbations. LightGCL enables structural refinement and captures global collaborative signals, achieving significant gains over state-of-the-art models across benchmark datasets. Our experiments also demonstrate improved fairness and resilience to popularity bias, making it well-suited for real-world recommender systems.
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