AI 中文总结
针对生成式推荐中引入思维链推理效果不稳定的问题，提出OneReason模型，通过语义对齐、结构化模板监督和强化学习提升推理效果，并举办挑战赛推动研究。
AI 中文摘要
生成式推荐在工业界和学术界引起了广泛关注，旨在构建更智能的系统以打造下一代推荐系统。在大语言模型显著发展的浪潮下，我们团队开发了基于语义ID的OneRec/OneRec-V2模型。这些模型已广泛应用于生产环境，并展示了自回归下一项预测范式在工业推荐系统中的扩展潜力。在OneRec成功的基础上，我们进一步探索了一系列模型，包括OneRec-Think、OpenOneRec和OneReason，这些模型将物品语义ID与自然语言在统一表示空间中连接起来，旨在释放自然语言思维链（CoT）推理在推荐中的潜力。然而，我们的初步研究发现，引入推理思维链并不总是能提升推荐性能。为解决这一问题，OneReason加强了物品与语言之间的语义对齐，引入了基于结构化模板的兴趣推理监督，并应用先进的强化学习技术，使推理对推荐更有益。作为构建推荐基础模型的前沿课题，我们相信这一主题具有重要的研究价值，并希望鼓励更多研究者共同探索。为此，我们与SIGIR 2026社区合作，组织了快手探索者LLM-Rec挑战赛2026：推理生成式推荐。
英文摘要
Generative recommendation, has been attracted a surge of attentions in industrial and academic research community, towards to build more smart system to build next-generation recommender. Under the significant developing wave of large language model, our team have been developed Semantic ID based OneRec/OneRec-V2. These models have been widely deployed in production and demonstrate the scaling potential of the autoregressive next-item prediction paradigm for industrial recommender systems. Building on the success of OneRec, we further explored a series of models, including OneRec-Think, OpenOneRec, and OneReason, that connect item Semantic IDs with natural language in a unified representation space and seek to unlock the potential of natural-language chain-of-thought (CoT) reasoning for recommendation. However, our preliminary works found that introducing reasoning CoT does not always improve the recommendation performance. To address this issue, OneReason strengthens the semantic alignment between items and language, introduces structured template-based supervision for interest reasoning, and applies advanced reinforcement learning techniques to make reasoning more beneficial to recommendation. As a frontier topic to building recommendation foundation models, we believe this topic has significant research value and hope to encourage more researchers to explore it together. To this end, together with the SIGIR 2026 community, we organized the KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation.
