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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.06780 |
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| _version_ | 1866916681232678912 |
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| author | Bai, Yong Xiang, Rui Li, Kaiyuan Tang, Yongxiang Cheng, Yanhua Liu, Xialong Jiang, Peng Gai, Kun |
| author_facet | Bai, Yong Xiang, Rui Li, Kaiyuan Tang, Yongxiang Cheng, Yanhua Liu, Xialong Jiang, Peng Gai, Kun |
| contents | Modeling holistic user interests is important for improving recommendation systems but is challenged by high computational cost and difficulty in handling diverse information with full behavior context. Existing search-based methods might lose critical signals during behavior selection. To overcome these limitations, we propose CHIME: A Compressive Framework for Holistic Interest Modeling. It uses adapted large language models to encode complete user behaviors with heterogeneous inputs. We introduce multi-granular contrastive learning objectives to capture both persistent and transient interest patterns and apply residual vector quantization to generate compact embeddings. CHIME demonstrates superior ranking performance across diverse datasets, establishing a robust solution for scalable holistic interest modeling in recommendation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06780 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CHIME: A Compressive Framework for Holistic Interest Modeling Bai, Yong Xiang, Rui Li, Kaiyuan Tang, Yongxiang Cheng, Yanhua Liu, Xialong Jiang, Peng Gai, Kun Information Retrieval Modeling holistic user interests is important for improving recommendation systems but is challenged by high computational cost and difficulty in handling diverse information with full behavior context. Existing search-based methods might lose critical signals during behavior selection. To overcome these limitations, we propose CHIME: A Compressive Framework for Holistic Interest Modeling. It uses adapted large language models to encode complete user behaviors with heterogeneous inputs. We introduce multi-granular contrastive learning objectives to capture both persistent and transient interest patterns and apply residual vector quantization to generate compact embeddings. CHIME demonstrates superior ranking performance across diverse datasets, establishing a robust solution for scalable holistic interest modeling in recommendation systems. |
| title | CHIME: A Compressive Framework for Holistic Interest Modeling |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2504.06780 |