SS4Rec: Continuous-Time Sequential Recommendation with State Space Models

TL;DR

SS4Rec introduces a hybrid state space model for continuous-time sequential recommendation, effectively capturing irregular time intervals and contextual dependencies to improve personalized user interest modeling.

Contribution

The paper presents SS4Rec, a novel hybrid state space model that integrates time-aware and relation-aware components for continuous-time recommendation, addressing limitations of previous discrete models.

Findings

Outperforms existing methods on five benchmark datasets.

Effectively models user interest with irregular time intervals.

Provides time-specific personalized recommendations.

Abstract

Sequential recommendation is a key area in the field of recommendation systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec.