Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at dealing with highly sparse data. Despite their success, most existing graph contrastive learning methods either perform stochastic augmentation (e.g., node/edge perturbation) on the user-item interaction graph, or rely on the heuristic-based augmentation techniques (e.g., user clustering) for generating contrastive views. We argue that these methods cannot well preserve the intrinsic semantic structures and are easily biased by the noise perturbation. In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders. Our model exclusively utilizes singular value decomposition for contrastive augmentation, which enables the unconstrained structural refinement with global collaborative relation modeling. Experiments conducted on several benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the superiority of LightGCL's robustness against data sparsity and popularity bias. The source code of our model is available at https://github.com/HKUDS/LightGCL.

翻译：摘要：图神经网络（GNN）是基于图的推荐系统中的一种强大学习范式。近年来，集成对比学习的GNN通过其数据增强方案在推荐任务中展现出优越性能，特别适用于处理高度稀疏的数据。然而，尽管取得了成功，现有的大多数图对比学习方法要么在用户-物品交互图上执行随机增强（例如节点/边扰动），要么依赖基于启发式的增强技术（例如用户聚类）生成对比视图。我们认为，这些方法无法很好地保留内在语义结构，且易受噪声扰动的影响产生偏差。本文提出了一种简单而有效的图对比学习范式LightGCL，有效缓解了这些损害基于对比学习推荐器通用性与鲁棒性的问题。该模型仅利用奇异值分解进行对比增强，从而能够通过全局协同关系建模实现无约束的结构优化。在多个基准数据集上的实验表明，我们的模型性能较现有最先进方法有显著提升。进一步的分析证明了LightGCL在应对数据稀疏性和流行度偏差方面的鲁棒性优势。模型源代码已公开在https://github.com/HKUDS/LightGCL。