Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential Recommendation

Weixin Li, Xiaolin Lin, Weike Pan, Zhong Ming

Abstract: Sequential recommendation has been widely used to predict users’ potential preferences by learning their dynamic user interests, for which most previous methods focus on capturing item-level dependencies. Despite the great success, they often overlook the stage-level interest dependencies. In real-world scenarios, user interests tend to be staged, e.g., following an item purchase, a user’s interests may undergo a transition into the subsequent phase. And there are intricate dependencies across different stages. Meanwhile, users’ behaviors are usually heterogeneous, including auxiliary behaviors (e.g., examinations) and target behaviors (e.g., purchases), which imply more fine-grained user interests. However, existing methods have limitations in explicitly modeling the relationships between the different types of behaviors. To address the above issues, we propose a novel framework, i.e., dynamic stage-aware user interest learning (DSUIL), for heterogeneous sequential recommendation, which is the first solution to model user interests in a cross-stage manner. Specifically, our DSUIL consists of four modules: (1) a dynamic graph construction module transforms a heterogeneous sequence into several subgraphs to model user interests in a stage-wise manner; (2) a dynamic graph convolution module dynamically learns item representations in each subgraph; (3) a behavior-aware subgraph representation learning module learns the heterogeneous dependencies between behaviors and aggregates item representations to represent the staged user interests; and (4) an interest evolving pattern extractor learns the users’ overall interests for the item prediction. Extensive experimental results on two public datasets show that our DSUIL performs significantly better than the state-of-the-art methods.