[2604.09439] TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation

Computer Science > Information Retrieval arXiv:2604.09439 (cs) [Submitted on 10 Apr 2026] Title:TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation Authors:Qingzhuo Wang, Leilei Wen, Juntao Chen, Kunyu Peng, Ruiyang Qin, Zhihua Wei, Wen Shen View a PDF of the paper titled TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation, by Qingzhuo Wang and 6 other authors View PDF HTML (experimental) 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 co...