Behavior-Type Aware Representation Learning for Multiplex Behavior Recommendation

Efficient recommender systems are essential for modeling user-item interactions, such as views, favorites, and purchases. However, two challenges remain: 1 Complex user-item interactions require a more informative method for modeling multiplex behavior patterns on representation learning. 2 Ignoring the effect of different interactions on the target interaction in recommender system scenarios. In this study, we propose a more informative framework, Behavior-Type Aware Representation Learning for Multiplex Behavior Recommendation BA-MBRec , to learn representations of users and items by mining behavior-aware patterns in feature encoding. Specifically, BA-MBRec is a powerful approach tailored to effectively encode nodes across various multiplex structures. It not only adaptively captures individual behavior-aware patterns but also discovers the interdependencies across these various patterns within multiplex heterogeneous networks by hierarchical modeling and cross-behavioral aggregators. Experiments on three real-world datasets demonstrate its superior performance, with improvements of 5.2 in HR@10 and 10.16 in NDCG@10 over state-of-the-art methods. Our empirical studies further demonstrate the great potential of this framework for capturing the multiplexity of users’ preferences in recommendation scenarios. Our implementation code is available in https: github.com sunshixx BA-MBRec tree master.