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.

翻译：本文提出了一种集成时间感知个性化、多兴趣个性化和解释个性化于一体的序列推荐模型TME-PSR（Time-aware, Multi-interest, and Explanation Personalization for Personalized Sequential Recommendation）。该模型综合考虑了不同用户在时间节奏偏好、多粒度潜在兴趣以及推荐与解释间个性化语义对齐方面的差异。具体而言，TME-PSR模型采用双视角门控时间编码器捕捉个性化时间节奏，设计了一种轻量级多头线性递归单元架构以实现高效细粒度子兴趣建模，并引入动态双分支互信息加权机制来达成推荐与解释间的个性化对齐。在真实数据集上的大量实验表明，本方法在较低的计算成本下，能够持续提升推荐准确性与解释质量。