Denoising Item Graph With Disentangled Learning for Recommendation

Recent years have witnessed the growth of Graph-based Collaborative Filtering (GCF) for high-performance recommendations, but the widely adopted user-item bipartite graphs are subject to deeper layers' over-smoothing effect and sparse user-item interactions when learning item representations. In this work, we introduce item graph, which regards items as nodes and connecting those that have ever co-occurred in transactions with edges, to preserve higher-order item relations while avoiding the drawbacks of bipartite graphs for item-based recommendation. To cope with the entangled semantics in the edges of an item graph, we first design a denoising scheme via a graph structure learning module with discrete sampling to drop noisy edges with respect to certain latent aspects, where multiple subgraphs can be yielded. We then implement graphical disentangled learning by imposing several structural regularizers that allow for macro conformity and micro divergence among the subgraphs. Finally, we propose a multi-graph fusion module to aggregate users' preferences in different subgraphs with a user-graph attention mechanism. Extensive experiments on 5 real-world datasets demonstrate the superiority of our method over 16 competitive baseline methods including the recently proposed GCF ones. Particularly, our method shows evident advantages in recommendation under data sparsity conditions.