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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.20730 |
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| _version_ | 1866916456842657792 |
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| author | Wang, Yejing Xu, Dong Zhao, Xiangyu Mao, Zhiren Xiang, Peng Yan, Ling Hu, Yao Zhang, Zijian Wei, Xuetao Liu, Qidong |
| author_facet | Wang, Yejing Xu, Dong Zhao, Xiangyu Mao, Zhiren Xiang, Peng Yan, Ling Hu, Yao Zhang, Zijian Wei, Xuetao Liu, Qidong |
| contents | GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. We design the dual group embedding space to offer a diverse perspective on group preferences by contrasting positive and negative patterns. On the individual level, GPRec identifies personal preferences from ID-like features and refines the obtained individual representations to be independent of group ones, thereby providing a robust complement to the group-level modeling. We also present various strategies for the flexible integration of GPRec into various DRS models. Rigorous testing of GPRec on three public datasets has demonstrated significant improvements in recommendation quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20730 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPRec: Bi-level User Modeling for Deep Recommenders Wang, Yejing Xu, Dong Zhao, Xiangyu Mao, Zhiren Xiang, Peng Yan, Ling Hu, Yao Zhang, Zijian Wei, Xuetao Liu, Qidong Information Retrieval Artificial Intelligence GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. We design the dual group embedding space to offer a diverse perspective on group preferences by contrasting positive and negative patterns. On the individual level, GPRec identifies personal preferences from ID-like features and refines the obtained individual representations to be independent of group ones, thereby providing a robust complement to the group-level modeling. We also present various strategies for the flexible integration of GPRec into various DRS models. Rigorous testing of GPRec on three public datasets has demonstrated significant improvements in recommendation quality. |
| title | GPRec: Bi-level User Modeling for Deep Recommenders |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2410.20730 |