End-to-End Time Interval-wise Segmentation for Sequential Recommendation

Abstract

Sequential recommendation aims to predict a user's next interaction based on their historical behavior. While recent models have achieved remarkable success, they often overlook time intervals between interactions or rely on fixed thresholds for session segmentation, which can lead to suboptimal results. To address these limitations, several approaches incorporate time intervals via relative positional embeddings or session segmentation based on fixed thresholds. However, these methods are highly sensitive to threshold selection and are prone to inaccurate segmentation. Inspired by these challenges, we propose TiSRec, a Time Interval-wise Segmentation framework that dynamically divides user sequences into Local Preference Blocks (LPBs) by selecting significant time intervals. TiSRec captures evolving user preferences through intra-block and inter-block encoders. Experiments on four real-world datasets demonstrate that TiSRec consistently outperforms state-of-the-art methods, and ablation studies confirm the effectiveness of LPBbased modeling.