用协同过滤增强大模型推荐，不训练也能精准匹配用户偏好。
Enhance Large Language Models as Recommendation Systems with Collaborative Filtering
- 用独立的协同过滤模型生成推荐评语，指导大模型优化输出。
- 在真实数据集上显著提升推荐质量，无需微调大模型。
- 适合希望低成本接入大模型推荐但需个性化的企业场景。
作为自然语言处理中的强大工具，大语言模型（LLMs）被用于生成推荐以精确匹配用户偏好并提升推荐质量。现有方法分为非微调和微调两类：非微调策略避免了在特定任务数据集上进一步训练预训练大模型所带来的高成本、耗时及专业门槛问题，但其缺乏特定业务或本地企业知识。据我们所知，现有非微调方法均未显式融合协同过滤——这一最成功的推荐技术之一。为此，本文提出一种基于批判的大型语言模型推荐系统（Critic-LLM-RS）。具体而言，我们训练一个独立的机器学习模型（称为Critic），该模型通过学习大量用户与物品间的交互行为来实现协同过滤推荐。Critic对大模型的推荐输出提供批判性反馈，从而显著优化推荐结果。大量实验证明，Critic-LLM-RS在真实数据集上具有显著有效性。
原文摘要 · Abstract (English)
As powerful tools in Natural Language Processing (NLP), Large Language Models (LLMs) have been leveraged for crafting recommendations to achieve precise alignment with user preferences and elevate the quality of the recommendations. The existing approaches implement both non-tuning and tuning strategies. Compared to following the tuning strategy, the approaches following the non-tuning strategy avoid the relatively costly, time-consuming, and expertise-requiring process of further training pre-trained LLMs on task-specific datasets, but they suffer the issue of not having the task-specific business or local enterprise knowledge. To the best of our knowledge, none of the existing approaches following the non-tuning strategy explicitly integrates collaborative filtering, one of the most successful recommendation techniques. This study aims to fill the gap by proposing critique-based LLMs as recommendation systems (Critic-LLM-RS). For our purpose, we train a separate machine-learning model called Critic that implements collaborative filtering for recommendations by learning from the interactions between many users and items. The Critic provides critiques to LLMs to significantly refine the recommendations. Extensive experiments have verified the effectiveness of Critic-LLM-RS on real datasets.
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