Multi-behavior Recommendation with Two-Level Graph Attentional Networks

Multi-behavior recommendation learns accurate embeddings of users and items with multiple types of interactions. Although existing multi-behavior recommendation methods have been proven effective, the following two insights are often neglected. First, the semantic strength of different types of behaviors is ignored. Second, these methods only consider the static preferences of users and the static feature of items. These limitations motivate us to propose a novel recommendation model AMR (Attentional Multi-behavior Recommendation) in this paper, which captures hidden relations in user-item interaction network by constructing multi-relation graphs with different behavior types. Specifically, the node-level attention aims to learn the importance of neighbors under specific behavior, while the behavior-level attention is able to learn the semantic strength of different behaviors. In addition, we learn the dynamic feature of target users and target items by modeling the dependency relation between them. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art recommendation methods.