Attention-guided graph convolutional network for multi-behavior recommendation

Abstract

Multi-behavior recommendation system aims to improve recommendation performance by using the interaction data of users′ multiple behaviors. Although some methods have explored the dependencies between different behaviors, there are also existing challenges: (1) user–item interactions have complex dependencies; (2) the dependencies between multiple behaviors vary due to users′ personalized preferences. To address these challenges, we propose a new model MB-AGCN (Attention-Guided Graph Convolutional Network for Multi-Behavior Recommendation), which considers personalized interaction patterns and cross-typed behavioral interdependencies. In the MB-AGCN framework, we take the different effects of multi-behavior information on predicting user preferences into account. We first model the user multi-behavior relationships with the attention mechanism to capture the personalized multi-behavior characteristics. Then, we explore the knowledge learned from the multi-behavior relationship modeling to generate a weight matrix that guides the graph neural network to learn the complex dependencies in different types of user–item interactions and capture the relationships between different types of behaviors. A comprehensive evaluation on three real-world datasets shows that MB-AGCN consistently outperforms state-of-the-art methods. Our codes will be available at https://github.com/3endurance/MB-AGCN.