让大模型更懂用户偏好，提升推荐精准度
Improving LLM-powered Recommendations with Personalized Information
- 引入用户偏好与物品感知的思维链分析
- 在推荐中显式利用个性化信息，效果更优
- 适合研究大模型推荐与个性化系统的人
现有基于大模型的推荐系统因缺乏显式的推理建模，未能有效发挥大模型的推理能力。本文提出一种名为CoT-Rec的流水线方法，将用户偏好分析和物品感知分析两种链式思维（CoT）过程融入推荐流程，从而增强大模型推理能力的利用。CoT-Rec包含两个阶段：(1) 个性化信息提取，从用户行为与物品描述中提取偏好与感知；(2) 个性化信息利用，将提取的信息注入大模型推荐过程。实验表明，CoT-Rec在多个数据集上均表现出优于基线的效果，具备提升推荐质量的潜力。代码已公开于https://github.com/jhliu0807/CoT-Rec。
原文摘要 · Abstract (English)
Due to the lack of explicit reasoning modeling, existing LLM-powered recommendations fail to leverage LLMs' reasoning capabilities effectively. In this paper, we propose a pipeline called CoT-Rec, which integrates two key Chain-of-Thought (CoT) processes -- user preference analysis and item perception analysis -- into LLM-powered recommendations, thereby enhancing the utilization of LLMs' reasoning abilities. CoT-Rec consists of two stages: (1) personalized information extraction, where user preferences and item perception are extracted, and (2) personalized information utilization, where this information is incorporated into the LLM-powered recommendation process. Experimental results demonstrate that CoT-Rec shows potential for improving LLM-powered recommendations. The implementation is publicly available at https://github.com/jhliu0807/CoT-Rec.
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