Heterogeneous Multi-Behavior Recommendation Based on Graph Convolutional Networks

Abstract

Traditional recommendation systems usually use single user-item interaction information, which ignore the multiple relationships that exist for other interactions (e.g., likes, clicks). Multi-behavioral recommendation models compensate for the shortcomings of traditional models. The existing multi-behavior recommendation models focus on obtaining behavioral information by distinguishing the interaction differences of multiple user behaviors but ignore the common preferences of items related to user different interactions. In this paper, we propose a Multi-Behavior Heterogeneous Contrastive learning Recommendation (MBHCR) model. Specifically, MBHCR stresses the information fusion between different interaction behaviors and differences in the behavior view. Firstly, we design a unified heterogeneous graph based on the complexity of user multi-behavioral interaction information to distinguish behavioral differences while preserving their complete semantic information. Secondly, we propose a multi-behavior relational aggregator component to model the unified heterogeneous graph only once to capture the potential common representations of users and their different interactions and to mitigate user sparsity. Thirdly, we also design a behavior comparison learning enhancer to complement the interaction differences between the user’s target behavior and auxiliary behaviors on the behavior view and extract valid information. Experiments were conducted on two real-world datasets to demonstrate the validity of MBHCR and designed ablation experiments to verify the contribution of model components.

Keywords

• Graph convolution networks
• heterogeneous graph
• multi-behavior
• recommendation system