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.

中文翻译：

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双边非对称图对比学习推荐

最近的方法利用图结构的用户-项目交互数据中的图对比学习进行协作过滤，并证明了它们在推荐任务中的有效性。然而，他们忽略了用户侧和物品侧节点关系密度的差异导致多跳图交互计算后双边节点上的图的适应性不同，这限制了现有模型达到理想的结果。为了解决这个问题，我们提出了一种称为双边不对称图对比学习（BusGCL）的推荐任务新框架，该框架考虑了用户-项目节点关系密度的双边不对称性，以便通过双边切片对比训练更好地进行切片用户和项目图推理。特别是，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合用户节点，由三个不同模块生成的嵌入：基于超图的GCN、GCN和扰动GCN，被分成两个用户侧和项目侧分别划分子视图，并根据节点间关系结构的特点选择性地组合成双边子视图对。此外，为了在聚合后对齐用户和项目嵌入的分布，利用分散损失来调整所有嵌入之间的相互距离以保持学习能力。在两个公共数据集上的综合实验证明了BusGCL相对于各种推荐方法的优越性。其他模型可以简单地利用我们的双边切片对比学习来增强推荐性能，而不会产生额外的费用。