Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
Lianghao Xia, Chao Huang, Yong Xu, Jian Pei
Abstract
Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications. Hence, sequential recommender systems have been developed to learn the dynamic user interests from the historical interactions for suggesting items. However, the interaction pattern encoding functions in most existing sequential recommender systems have thus far focused on singular type of user-item interactions. In practice, user-item interactive behaviors are often multi-typed (e.g., browse, add-to-favorite, purchase) with complex cross-type behavior inter-dependencies. Learning from informative representations of users and items based on their multi-typed interaction data, is of great importance to accurately characterize the time-evolving user preference. This work tackles the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. Towards this end, we propose a Temporal Graph-Structured Transformer (TGST) to jointly capture dynamic short-term and long-range user-item interactive patterns, by exploring the evolving structural dependency across different types of behaviors. This new TGST framework endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context. Extensive experiments on real-world datasets indicate that our method consistently outperforms various state-of-the-art baselines. Further experimental studies show that TGST can offer insights of interpretable explanations by capturing multi-behavioral patterns in a dynamic environment.