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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.09439 |
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| _version_ | 1866913021684613120 |
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| author | Wang, Qingzhuo Wen, Leilei Chen, Juntao Peng, Kunyu Qin, Ruiyang Wei, Zhihua Shen, Wen |
| author_facet | Wang, Qingzhuo Wen, Leilei Chen, Juntao Peng, Kunyu Qin, Ruiyang Wei, Zhihua Shen, Wen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09439 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation Wang, Qingzhuo Wen, Leilei Chen, Juntao Peng, Kunyu Qin, Ruiyang Wei, Zhihua Shen, Wen Information Retrieval Artificial Intelligence 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. |
| title | TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2604.09439 |