Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks. However, they ignore that the difference relation density of nodes between the user- and item-side causes the adaptability of graphs on bilateral nodes to be different after multi-hop graph interaction calculation, which limits existing models to achieve ideal results. To solve this issue, we propose a novel framework for recommendation tasks called Bilateral Unsymmetrical Graph Contrastive Learning (BusGCL) that consider the bilateral unsymmetry on user-item node relation density for sliced user and item graph reasoning better with bilateral slicing contrastive training. Especially, taking into account the aggregation ability of hypergraph-based graph convolutional network (GCN) in digging implicit similarities is more suitable for user nodes, embeddings generated from three different modules: hypergraph-based GCN, GCN and perturbed GCN, are sliced into two subviews by the user- and item-side respectively, and selectively combined into subview pairs bilaterally based on the characteristics of inter-node relation structure. Furthermore, to align the distribution of user and item embeddings after aggregation, a dispersing loss is leveraged to adjust the mutual distance between all embeddings for maintaining learning ability. Comprehensive experiments on two public datasets have proved the superiority of BusGCL in comparison to various recommendation methods. Other models can simply utilize our bilateral slicing contrastive learning to enhance recommending performance without incurring extra expenses.

翻译：近期方法利用图结构化用户-项目交互数据中的图对比学习进行协同过滤，并在推荐任务中展现出有效性。然而，这些方法忽略了用户侧与项目侧节点间关系密度的差异导致多跳图交互计算后图对双侧节点的适应性不同，这限制了现有模型获得理想结果。为解决该问题，我们提出一种面向推荐任务的新型框架——双边非对称图对比学习（BusGCL），该框架考虑用户-项目节点关系密度的双边非对称性，通过双边切片对比训练实现更优的用户与项目图推理。具体而言，考虑到基于超图的图卷积网络（GCN）在挖掘隐式相似性方面的聚合能力更适用于用户节点，由超图GCN、GCN和扰动GCN三个不同模块生成的嵌入分别按用户侧和项目侧切片为两个子视图，并根据节点间关系结构特征双向选择性地组合为子视图对。此外，为对齐聚合后用户与项目嵌入的分布，引入分散损失调整所有嵌入间的相互距离以维持学习能力。在两类公开数据集上的综合实验证明，相较于多种推荐方法，BusGCL具有优越性。其他模型可直接采用我们的双边切片对比学习提升推荐性能，而无需额外开销。