用于推荐的双侧非对称图对比学习
Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation
AI总结：
针对用户与物品节点关系密度差异导致的图适应性受限问题，提出双侧非对称图对比学习框架BusGCL，通过多模块嵌入切片与组合及分散损失对齐分布，有效提升了推荐性能且无额外开销。
摘要：
近期的方法在图结构的用户-物品交互数据中利用图对比学习进行协同过滤，并在推荐任务中证明了其有效性。然而，它们忽略了用户侧与物品侧之间节点关系密度的差异，导致在多跳图交互计算后，图在双侧节点上的适应性不同，这限制了现有模型取得理想效果。为了解决这一问题，我们提出了一种用于推荐任务的新框架，称为双侧非对称图对比学习（BusGCL），该框架考虑了用户-物品节点关系密度上的双侧非对称性，以便通过双侧切片对比训练更好地对切片的用户和物品图进行推理。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，从三个不同模块（基于超图的GCN、GCN和扰动的GCN）生成的嵌入分别按用户侧和物品侧被切片成两个子视图，并根据节点间关系结构的特征在双侧选择性地组合成子视图对。此外，为了对齐聚合后用户和物品嵌入的分布，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。在两个公开数据集上的综合实验证明了BusGCL相比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侧切片对比学习来增强推荐性能，而不会产生额外开销。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
