用大模型推理解决新物品推荐冷启动问题，效果优于现有系统。
LLM Reasoning for Cold-Start Item Recommendation
- 利用大模型推理能力，从少量交互中推断用户偏好。
- 在真实数据上，性能比现有系统最高提升8%。
- 适合新商品上线或低频物品推荐场景使用。
大型语言模型（LLMs）凭借其强大的推理能力和广泛的知识库，在推荐系统中展现出巨大潜力。然而，现有研究多聚焦于用户-物品交互数据丰富的暖启动场景，对冷启动场景——即交互稀疏导致协同过滤失效的情况——关注不足。为此，本文提出针对奈飞（Netflix）领域冷启动物品推荐的新颖推理策略。该方法利用大模型的推理能力，有效推断用户对新上线或极少互动物品的偏好。我们系统评估了监督微调、基于强化学习的微调，以及两者的混合方法，以优化推荐表现。在真实数据上的大量实验表明，该方法在冷启动推荐场景下具有显著的方法有效性与实际性能提升。值得注意的是，基于推理的微调模型在某些情况下，比奈飞当前生产级排序模型性能高出最多8%。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown significant potential for improving recommendation systems through their inherent reasoning capabilities and extensive knowledge base. Yet, existing studies predominantly address warm-start scenarios with abundant user-item interaction data, leaving the more challenging cold-start scenarios, where sparse interactions hinder traditional collaborative filtering methods, underexplored. To address this limitation, we propose novel reasoning strategies designed for cold-start item recommendations within the Netflix domain. Our method utilizes the advanced reasoning capabilities of LLMs to effectively infer user preferences, particularly for newly introduced or rarely interacted items. We systematically evaluate supervised fine-tuning, reinforcement learning-based fine-tuning, and hybrid approaches that combine both methods to optimize recommendation performance. Extensive experiments on real-world data demonstrate significant improvements in both methodological efficacy and practical performance in cold-start recommendation contexts. Remarkably, our reasoning-based fine-tuned models outperform Netflix's production ranking model by up to 8% in certain cases.
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