arXiv:2606.06260v1 Announce Type: cross Abstract: Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. However, these generative models can only benefit from the scaling advantage, while their reasoning ability is hard to activate, since we cannot construct meaningful Chain-of-Thought (CoT) sequences consisting of itemic tokens only. Inspired by the success of the reasoning-style ``think before answer'' paradigm in the LLM field, we conduct preliminary studies (i.e., OneRec
The paper leverages recent advancements in large language models' reasoning capabilities to address limitations in generative recommendation systems, indicating a growing convergence of AI techniques.
This technical report signifies a crucial step in enhancing the reasoning abilities of recommendation AI, which could profoundly impact how digital platforms personalize content and products.
Recommendation models are evolving beyond mere scaling to incorporate more sophisticated 'think before answer' reasoning, potentially leading to more nuanced and effective personalization.
- · E-commerce platforms
- · Short-video and live-streaming services
- · Advertising technology companies
- · AI research organizations
- · Traditional recommendation systems lacking reasoning capabilities
- · Platforms overly reliant on simplistic matching algorithms
Generative recommendation models will begin integrating advanced reasoning capabilities inspired by LLMs.
Improved recommendation efficacy could lead to higher user engagement and conversion rates across various digital services.
This paradigm shift might spawn new business models centered on highly personalized and context-aware digital experiences, pushing the boundaries of autonomous content curation.
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Read at arXiv cs.CL