QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911434379624448 |
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| author | Xia, Tian Zhang, Jiaqi Liu, Yueyang Dou, Hongjian Yin, Tingya Cao, Jiangxia Liang, Xulei Xie, Tianlu Liu, Lihao Chen, Xiang Wang, Shen Lao, Changxin Gan, Haixiang Yu, Jinkai Cen, Keting Hao, Lu Zhang, Xu Zhong, Qiqiang Sun, Zhongbo Wang, Yiyu Yang, Shuang Wen, Mingxin Wu, Xiangyu Liu, Shaoguo Gao, Tingting Liu, Zhaojie Li, Han Gai, Kun |
| author_facet | Xia, Tian Zhang, Jiaqi Liu, Yueyang Dou, Hongjian Yin, Tingya Cao, Jiangxia Liang, Xulei Xie, Tianlu Liu, Lihao Chen, Xiang Wang, Shen Lao, Changxin Gan, Haixiang Yu, Jinkai Cen, Keting Hao, Lu Zhang, Xu Zhong, Qiqiang Sun, Zhongbo Wang, Yiyu Yang, Shuang Wen, Mingxin Wu, Xiangyu Liu, Shaoguo Gao, Tingting Liu, Zhaojie Li, Han Gai, Kun |
| contents | With the evolution of large language models (LLMs), there is growing interest in leveraging their rich semantic understanding to enhance industrial recommendation systems (RecSys). Traditional RecSys relies on ID-based embeddings for user sequence modeling in the General Search Unit (GSU) and Exact Search Unit (ESU) paradigm, which suffers from low information density, knowledge isolation, and weak generalization ability. While LLMs offer complementary strengths with dense semantic representations and strong generalization, directly applying LLM embeddings to RecSys faces critical challenges: representation unmatch with business objectives and representation unlearning end-to-end with downstream tasks. In this paper, we present QARM V2, a unified framework that bridges LLM semantic understanding with RecSys business requirements for user sequence modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08559 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling Xia, Tian Zhang, Jiaqi Liu, Yueyang Dou, Hongjian Yin, Tingya Cao, Jiangxia Liang, Xulei Xie, Tianlu Liu, Lihao Chen, Xiang Wang, Shen Lao, Changxin Gan, Haixiang Yu, Jinkai Cen, Keting Hao, Lu Zhang, Xu Zhong, Qiqiang Sun, Zhongbo Wang, Yiyu Yang, Shuang Wen, Mingxin Wu, Xiangyu Liu, Shaoguo Gao, Tingting Liu, Zhaojie Li, Han Gai, Kun Information Retrieval With the evolution of large language models (LLMs), there is growing interest in leveraging their rich semantic understanding to enhance industrial recommendation systems (RecSys). Traditional RecSys relies on ID-based embeddings for user sequence modeling in the General Search Unit (GSU) and Exact Search Unit (ESU) paradigm, which suffers from low information density, knowledge isolation, and weak generalization ability. While LLMs offer complementary strengths with dense semantic representations and strong generalization, directly applying LLM embeddings to RecSys faces critical challenges: representation unmatch with business objectives and representation unlearning end-to-end with downstream tasks. In this paper, we present QARM V2, a unified framework that bridges LLM semantic understanding with RecSys business requirements for user sequence modeling. |
| title | QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2602.08559 |