LLM Reasoning for Cold-Start Item Recommendation

TL;DR

This paper explores leveraging Large Language Models' reasoning abilities to improve cold-start item recommendations, especially for new or rarely interacted items, outperforming existing models in real-world Netflix data.

Contribution

It introduces novel reasoning strategies and fine-tuning approaches for LLMs tailored to cold-start recommendation challenges, a less explored area.

Findings

Models outperform Netflix's production ranking by up to 8%.

Reasoning-based fine-tuning enhances recommendation accuracy.

Hybrid approaches yield the best performance.

Abstract

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.