Attention-aware graph contrastive learning with topological relationship for recommendation

Abstract

Recommender systems are a vital tool to guide the overwhelming amount of online information for users, which has been successfully applied to online retail platforms, social networks, etc. Recently, contrastive learning has revealed outstanding performance in recommendation by data augmentation strategies to handle highly sparse data. Most existing work fails to leverage the original network’s topology to construct attention-aware modules that identify user–item interaction importance for guiding node aggregation while preserving key semantics and reducing noise in the reconstructed graph during data augmentation. In this paper, our work proposes an Attention-aware Graph Contrastive Learning architecture with Topological Relationship (AteGCL) for recommendation. In particular, our AteGCL proposes an attention-aware mechanism with topological relationships to learn the importance between users and items for extracting the local graph dependency, which identifies the importance between nodes by constructing an attention-aware matrix into graph convolutional networks using a random walk with a restart strategy for generating node feature aggregation. We then employ principal component analysis (PCA) for contrastive augmentation and utilize the attention-aware matrix to ease noise from the reconstructed graph generated by PCA and to generate a new view with global collaborative relationships and less noise. Comprehensive experiments on three real-world user–item networks reveal the superiority of our AteGCL over diverse state-of-the-art recommendation approaches. Our code is available at https://github.com/ZZHCodeZera/AteGCL.