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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.19158 |
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| _version_ | 1866917224993783808 |
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| author | Liu, Qijiong Fan, Lu Liu, Zhongzhou Dong, Xiaoyu Luo, Yuankai An, Guoyuan Chen, Nuo Guo, Wei Liu, Yong Wu, Xiao-Ming |
| author_facet | Liu, Qijiong Fan, Lu Liu, Zhongzhou Dong, Xiaoyu Luo, Yuankai An, Guoyuan Chen, Nuo Guo, Wei Liu, Yong Wu, Xiao-Ming |
| contents | Although generative recommenders demonstrate improved performance with longer sequences, their real-time deployment is hindered by substantial computational costs. To address this challenge, we propose a simple yet effective method for compressing long-term user histories by leveraging inherent item categorical features, thereby preserving user interests while enhancing efficiency. Experiments on two large-scale datasets demonstrate that, compared to the influential HSTU model, our approach achieves up to a 6x reduction in computational cost and up to 39% higher accuracy at comparable cost (i.e., similar sequence length). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19158 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accelerating Generative Recommendation via Simple Categorical User Sequence Compression Liu, Qijiong Fan, Lu Liu, Zhongzhou Dong, Xiaoyu Luo, Yuankai An, Guoyuan Chen, Nuo Guo, Wei Liu, Yong Wu, Xiao-Ming Information Retrieval Although generative recommenders demonstrate improved performance with longer sequences, their real-time deployment is hindered by substantial computational costs. To address this challenge, we propose a simple yet effective method for compressing long-term user histories by leveraging inherent item categorical features, thereby preserving user interests while enhancing efficiency. Experiments on two large-scale datasets demonstrate that, compared to the influential HSTU model, our approach achieves up to a 6x reduction in computational cost and up to 39% higher accuracy at comparable cost (i.e., similar sequence length). |
| title | Accelerating Generative Recommendation via Simple Categorical User Sequence Compression |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2601.19158 |