TME-PSR模型融合时间、兴趣与解释，实现更精准的个性化推荐。
TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation
- 用双视角门控时间编码捕捉用户独特的时间偏好节奏。
- 轻量级多头线性循环单元实现细粒度兴趣建模且效率更高。
- 动态双分支互信息加权机制让推荐与解释更贴合用户需求。
本文提出一种面向个性化序列推荐的TME-PSR模型，融合时间感知、多兴趣感知与解释感知三方面。该模型采用双视角门控时间编码器捕获用户的个性化时间节奏，设计轻量级多头线性循环单元（Linear Recurrent Unit）以高效实现细粒度子兴趣建模，并引入动态双分支互信息加权机制，实现推荐与解释之间的个性化语义对齐。在真实数据集上的大量实验表明，该方法在保持较低计算成本的前提下，持续提升推荐准确率与解释质量。
原文摘要 · Abstract (English)
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.
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