Multiplex Graph Neural Networks for Multi-behavior Recommendation

Abstract

This paper focuses on the multi-behavior recommendation problem, i.e., generating personalized recommendation based on multiple types of user behaviors. Methods proposed recently usually leverage the ordinal assumption, which means that users? different types of behaviors should take place in a fixed order. However, this assumption may be too strong in some scenarios. In this paper, a more general model named Multiplex Graph Neural Network (MGNN) is proposed as a remedy. MGNN tackles the multi-behavior recommendation problem from a novel perspective, i.e., the perspective of link prediction in multiplex networks. By taking advantage of both the multiplex network structure and graph representation learning techniques, MGNN learns shared embeddings and behavior-specific embeddings for users and items to model the collective effect of multiple types of behaviors. Experiments conducted on both ordinal-behavior datasets and generic-behavior datasets demonstrate the effectiveness of the proposed MGNN model.