Dual-disentangle Framework for Diversified Sequential Recommendation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908479192563712 |
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| author | Zhang, Haoran Liu, Jingtong Deng, Jiangzhou Guo, Junpeng |
| author_facet | Zhang, Haoran Liu, Jingtong Deng, Jiangzhou Guo, Junpeng |
| contents | Sequential recommendation predicts user preferences over time and has achieved remarkable success. However, the growing length of user interaction sequences and the complex entanglement of evolving user interests and intentions introduce significant challenges to diversity. To address these, we propose a model-agnostic Dual-disentangle framework for Diversified Sequential Recommendation (DDSRec). The framework refines user interest and intention modeling by adopting disentangling perspectives in interaction modeling and representation learning, thereby balancing accuracy and diversity in sequential recommendations. Extensive experiments on multiple public datasets demonstrate the effectiveness and superiority of DDSRec in terms of accuracy and diversity for sequential recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual-disentangle Framework for Diversified Sequential Recommendation Zhang, Haoran Liu, Jingtong Deng, Jiangzhou Guo, Junpeng Information Retrieval Sequential recommendation predicts user preferences over time and has achieved remarkable success. However, the growing length of user interaction sequences and the complex entanglement of evolving user interests and intentions introduce significant challenges to diversity. To address these, we propose a model-agnostic Dual-disentangle framework for Diversified Sequential Recommendation (DDSRec). The framework refines user interest and intention modeling by adopting disentangling perspectives in interaction modeling and representation learning, thereby balancing accuracy and diversity in sequential recommendations. Extensive experiments on multiple public datasets demonstrate the effectiveness and superiority of DDSRec in terms of accuracy and diversity for sequential recommendations. |
| title | Dual-disentangle Framework for Diversified Sequential Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.03172 |