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.
@article{arxiv.2604.09439,
title = {TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation},
author = {Qingzhuo Wang and Leilei Wen and Juntao Chen and Kunyu Peng and Ruiyang Qin and Zhihua Wei and Wen Shen},
journal= {arXiv preprint arXiv:2604.09439},
year = {2026}
}