OneRec-Think: In-Text Reasoning for Generative Recommendation

• URL: http://arxiv.org/abs/2510.11639v1
• Date: Mon, 13 Oct 2025 17:20:13 GMT
• Title: OneRec-Think: In-Text Reasoning for Generative Recommendation
• Abstract summary: OneRec-Think is a unified framework that seamlessly integrates dialogue, reasoning, and personalized recommendation.<n>Our proposed "Think-Ahead" architecture enables effective industrial deployment on Kuaishou, achieving a 0.159% gain in APP Stay Time.
• Score: 55.53292983432484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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