Multi-interest sequential recommendation with contrastive learning and temporal analysis

Sequential recommendation systems aim to forecast the subsequent item of interest to users by analyzing their historical behaviors. While existing approaches, which employ attention mechanisms, have significantly advanced by capturing users' multiple interests, they encounter two primary challenges. Firstly, they often fail to effectively capture the transient shifts in users' interests across a sequence of items and neglect the interdependencies among these items, leading to a misalignment between the identified and actual interests. Secondly, conventional multi-interest models struggle to ensure that the identified interests are distinct, which results in overly similar interests that may not adequately satisfy user requirements. To address these issues, we propose a novel multi-interest recommendation method, which models the temporal features and user's preference features from the user level. In order to capture short-term variations in interest, we introduce a time period module to encode the behavioral intervals between items and capture the periodicity of users clicking on similar items by extracting temporal information. In addition, we integrate similar types of items into the interest subgraph through preference feature extraction to capture users' short-term changes in relevance term interests, and incorporate contrastive learning to enhance the differences between the captured interests. Extensive experiments conducted on two datasets Amazon Books and Taobao show that the model outperforms current state-of-the-art methods.