TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation
This work addresses personalized sequential recommendation for users, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of sequential recommendation by integrating time-aware, multi-interest, and explanation personalization, resulting in improved recommendation accuracy and explanation quality with lower computational cost.
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.