TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation
2026-04-10 • Information Retrieval
Information RetrievalArtificial Intelligence
AI summaryⓘ
The authors developed a recommendation system that better understands individual user preferences by considering when users like to engage with content, recognizing multiple specific interests they have, and tailoring explanations to match those interests. They use new techniques to track users' timing habits, identify fine-grained interests more efficiently, and connect recommendations with personalized explanations. Experiments show their approach improves the accuracy of suggestions and the quality of explanations while using less computing power.
Sequential recommendationTime-aware personalizationMulti-interest modelingExplanation personalizationTemporal rhythmsRecurrent neural networksMutual informationUser modeling
Authors
Qingzhuo Wang, Leilei Wen, Juntao Chen, Kunyu Peng, Ruiyang Qin, Zhihua Wei, Wen Shen
Abstract
In this paper, we propose a sequential recommendation model that integrates Time-aware personalization, Multi-interest personalization, and Explanation personalization for Personalized Sequential Recommendation (TME-PSR). That is, we consider the differences across different users in temporal rhythm preference, multiple fine-grained latent interests, and the personalized semantic alignment between recommendations and explanations. Specifically, the proposed TME-PSR model employs a dual-view gated time encoder to capture personalized temporal rhythms, a lightweight multihead Linear Recurrent Unit architecture that enables fine-grained sub-interest modeling with improved efficiency, and a dynamic dual-branch mutual information weighting mechanism to achieve personalized alignment between recommendations and explanations. Extensive experiments on real-world datasets demonstrate that our method consistently improves recommendation accuracy and explanation quality, at a lower computational cost.