RETSR: An Effective Review-Enhanced and Time-Aware Sequential Recommendation Framework
Weijin Wang, Jin Li, Yilei Wang, Zhongwang Zhang · 2022
Side information has been demonstrated effective for recommendation by many existing works. However, even recent advancements can hardly extract the rich interactive knowledge and accurately model users' true interests due to ignorance of irregularities of reviews or temporal information, e.g., uneven time intervals. Therefore in this paper, we propose a Review-Enhanced Time-aware Sequential Recommendation framework (called RETSR) including review selector, long/short-term preferences extractors, etc. It can effectively exploit rich sequential, semantic, and temporal information to solve the aforementioned issues. More specifically, a review selector is first designed to select some important comments for obtaining users' long-term preferences and items' essential properties. Furthermore, users' short-term preferences are obtained by extracting meaningful time-sensitive features from two perspectives (i.e., reviews and interactions). Finally, extensive experiments show our framework significantly outperforms eleven baselines or state-of-the-art models on three popular real-world benchmarks, revealing its power for sequential recommendation tasks.