Attention-guided graph convolutional network for multi-behavior recommendation

Xingchen Peng, Jing Sun, Mingshi Yan, Fuming Sun, Fasheng Wang

Abstract: Multi-behavior recommendation system aims to improve recommendation performance by using the interaction data of users′<math><msup is="true"><mrow is="true"></mrow><mrow is="true"><mo is="true">′</mo></mrow></msup></math> 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′<math><msup is="true"><mrow is="true"></mrow><mrow is="true"><mo is="true">′</mo></mrow></msup></math> 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.

External IDs:dblp:journals/kbs/PengSYSW23