OneRec-Think: In-Text Reasoning for Generative 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_ | 1866912700763734016 |
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| author | Liu, Zhanyu Wang, Shiyao Wang, Xingmei Zhang, Rongzhou Deng, Jiaxin Bao, Honghui Zhang, Jinghao Li, Wuchao Zheng, Pengfei Wu, Xiangyu Hu, Yifei Hu, Qigen Luo, Xinchen Ren, Lejian Zhang, Zixing Wang, Qianqian Cai, Kuo Wu, Yunfan Cheng, Hongtao Cheng, Zexuan Ren, Lu Wang, Huanjie Su, Yi Tang, Ruiming Gai, Kun Zhou, Guorui |
| author_facet | Liu, Zhanyu Wang, Shiyao Wang, Xingmei Zhang, Rongzhou Deng, Jiaxin Bao, Honghui Zhang, Jinghao Li, Wuchao Zheng, Pengfei Wu, Xiangyu Hu, Yifei Hu, Qigen Luo, Xinchen Ren, Lejian Zhang, Zixing Wang, Qianqian Cai, Kuo Wu, Yunfan Cheng, Hongtao Cheng, Zexuan Ren, Lu Wang, Huanjie Su, Yi Tang, Ruiming Gai, Kun Zhou, Guorui |
| contents | The powerful generative capacity of Large Language Models (LLMs) has instigated a paradigm shift in recommendation. However, existing generative models (e.g., OneRec) operate as implicit predictors, critically lacking the capacity for explicit and controllable reasoning-a key advantage of LLMs. To bridge this gap, we propose OneRec-Think, a unified framework that seamlessly integrates dialogue, reasoning, and personalized recommendation. OneRec-Think incorporates: (1) Itemic Alignment: cross-modal Item-Textual Alignment for semantic grounding; (2) Reasoning Activation: Reasoning Scaffolding to activate LLM reasoning within the recommendation context; and (3) Reasoning Enhancement, where we design a recommendation-specific reward function that accounts for the multi-validity nature of user preferences. Experiments across public benchmarks show state-of-the-art performance. Moreover, our proposed "Think-Ahead" architecture enables effective industrial deployment on Kuaishou, achieving a 0.159\% gain in APP Stay Time and validating the practical efficacy of the model's explicit reasoning capability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11639 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OneRec-Think: In-Text Reasoning for Generative Recommendation Liu, Zhanyu Wang, Shiyao Wang, Xingmei Zhang, Rongzhou Deng, Jiaxin Bao, Honghui Zhang, Jinghao Li, Wuchao Zheng, Pengfei Wu, Xiangyu Hu, Yifei Hu, Qigen Luo, Xinchen Ren, Lejian Zhang, Zixing Wang, Qianqian Cai, Kuo Wu, Yunfan Cheng, Hongtao Cheng, Zexuan Ren, Lu Wang, Huanjie Su, Yi Tang, Ruiming Gai, Kun Zhou, Guorui Information Retrieval The powerful generative capacity of Large Language Models (LLMs) has instigated a paradigm shift in recommendation. However, existing generative models (e.g., OneRec) operate as implicit predictors, critically lacking the capacity for explicit and controllable reasoning-a key advantage of LLMs. To bridge this gap, we propose OneRec-Think, a unified framework that seamlessly integrates dialogue, reasoning, and personalized recommendation. OneRec-Think incorporates: (1) Itemic Alignment: cross-modal Item-Textual Alignment for semantic grounding; (2) Reasoning Activation: Reasoning Scaffolding to activate LLM reasoning within the recommendation context; and (3) Reasoning Enhancement, where we design a recommendation-specific reward function that accounts for the multi-validity nature of user preferences. Experiments across public benchmarks show state-of-the-art performance. Moreover, our proposed "Think-Ahead" architecture enables effective industrial deployment on Kuaishou, achieving a 0.159\% gain in APP Stay Time and validating the practical efficacy of the model's explicit reasoning capability. |
| title | OneRec-Think: In-Text Reasoning for Generative Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2510.11639 |