LLM Reasoning for Cold-Start Item Recommendation
This addresses the challenge of recommending new or rarely interacted items for users in platforms like Netflix, representing an incremental improvement over existing LLM-based methods.
The paper tackles the problem of cold-start item recommendation, where sparse user-item interactions hinder traditional methods, by proposing novel reasoning strategies using LLMs, resulting in models that outperform Netflix's production ranking model by up to 8% in some cases.
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.