MBDL: Exploring dynamic dependency among various types of behaviors for recommendation

Abstract

Users have various behaviors on items, including page view, tag-as-favorite, add-to-cart, and purchase in online shopping platforms. These various types of behaviors reflect users’ different intentions, which also help learn their preferences on items in a recommender system. Although some multi-behavior recommendation methods have been proposed, two significant challenges have not been widely noticed: (i) capturing heterogeneous and dynamic preferences of users simultaneously from different types of behaviors; (ii) modeling the dynamic dependency among various types of behaviors. To overcome the above challenges, we propose a novel multi-behavior dynamic dependency learning method (MBDL) to explore the heterogeneity and dependency among various types of behavior sequences for recommendation. In brief, MBDL first uses a dual-channel interest encoder to learn the long-term interest representations and the evolution of short-term interests from the behavior-aware item sequences. Then, MBDL adopts a contrastive learning method to preserve the consistency of user’s long-term behavioral patterns, and a multi-head attention network to capture the dynamic dependency among short-term interactive behaviors. Finally, MBDL adaptively integrates the influence of long- and short-term interests to predict future user–item interactions. Experiments on two real-world datasets show that the proposed MBDL method outperforms state-of-the-art methods significantly on recommendation accuracy. Further ablation studies demonstrate the effectiveness of our model and the benefits of learning dynamic dependency among types of behaviors.