Graph neural network(GNN) has been a powerful approach in collaborative filtering(CF) due to its ability to model high-order user-item relationships. Recently, to alleviate the data sparsity and enhance representation learning, many efforts have been conducted to integrate contrastive learning(CL) with GNNs. Despite the promising improvements, the contrastive view generation based on structure and representation perturbations in existing methods potentially disrupts the collaborative information in contrastive views, resulting in limited effectiveness of positive alignment. To overcome this issue, we propose CoGCL, a novel framework that aims to enhance graph contrastive learning by constructing contrastive views with stronger collaborative information via discrete codes. The core idea is to map users and items into discrete codes rich in collaborative information for reliable and informative contrastive view generation. To this end, we initially introduce a multi-level vector quantizer in an end-to-end manner to quantize user and item representations into discrete codes. Based on these discrete codes, we enhance the collaborative information of contrastive views by considering neighborhood structure and semantic relevance respectively. For neighborhood structure, we propose virtual neighbor augmentation by treating discrete codes as virtual neighbors, which expands an observed user-item interaction into multiple edges involving discrete codes. Regarding semantic relevance, we identify similar users/items based on shared discrete codes and interaction targets to generate the semantically relevant view. Through these strategies, we construct contrastive views with stronger collaborative information and develop a triple-view graph contrastive learning approach. Extensive experiments on four public datasets demonstrate the effectiveness of our proposed approach.

翻译：图神经网络(GNN)因其能够建模高阶用户-物品关系，已成为协同过滤(CF)中的强大方法。近年来，为缓解数据稀疏性并增强表示学习，许多研究致力于将对比学习(CL)与GNN相结合。尽管取得了显著改进，但现有方法中基于结构和表示扰动的对比视图生成可能会破坏对比视图中的协同信息，导致正样本对齐效果有限。为解决这一问题，我们提出CoGCL——一种通过离散编码构建具有更强协同信息的对比视图来增强图对比学习的新型框架。其核心思想是将用户和物品映射到富含协同信息的离散编码中，以生成可靠且信息丰富的对比视图。为此，我们首先以端到端方式引入多级向量量化器，将用户和物品表示量化为离散编码。基于这些离散编码，我们分别通过考虑邻域结构和语义相关性来增强对比视图的协同信息。针对邻域结构，我们提出虚拟邻居增强方法，将离散编码视为虚拟邻居，从而将观测到的用户-物品交互扩展为涉及离散编码的多条边。关于语义相关性，我们基于共享离散编码和交互目标识别相似用户/物品，以生成语义相关视图。通过这些策略，我们构建了具有更强协同信息的对比视图，并开发了三视图图对比学习方法。在四个公开数据集上的大量实验证明了所提方法的有效性。