一种基于LLM-RAG的健康饮食指数指导的个性化食品推荐方法
An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations
AI总结：
本文提出一种结合标准化营养数据库与大语言模型的LLM-RAG框架，通过健康饮食指数（HEI）指导实现个性化食品推荐，实验结果显示HEI平均提升6.45，用户HEI超过50的比例提升至61.26%。
摘要：
饮食质量是慢性疾病风险的主要决定因素。人工智能的进步使食品推荐系统能够根据用户偏好和健康目标调整建议。然而，大多数现有系统依赖松散编纂的食品数据库，并且与验证指数的连接有限。本文提出了一种基于健康饮食指数（HEI）指导的检索增强生成（RAG）框架，结合标准化营养数据库和大语言模型（LLMs）进行个性化食品推荐。我们的方法将检索锚定在国家健康和营养检查调查（NHANES）和食物模式等价数据库（FPED）上。通过FPED派生的文本描述构建食品级别的嵌入空间。对于每个实体，系统计算基础HEI分数，检索候选食品以提供摄入建议，并估算简单替代或添加对HEI的影响。一个使用预训练OpenAI LLM实例化的受限RAG管道生成基于营养概况和HEI贡献的个性化推荐和来源。模拟结果表明，HEI平均提升6.45，HEI超过50的用户比例从45.12增加到61.26%。分位数分析揭示了HEI分布中的一致改善。我们的发现表明，基于LLM-RAG的AI系统能够支持更精确、可解释和个性化的营养指导以提高饮食质量。
英文摘要：
Diet quality is a leading determinant of chronic disease risk. Advances in artificial intelligence (AI) have enabled food recommendation systems to adapt suggestions to user preferences and health goals. However, most current systems rely on loosely curated food databases and provide limited connection to a validated index. In this study, we propose a Healthy Eating Index (HEI) informed retrieval-augmented generation (RAG) framework that combines standardized nutrition databases with large language models (LLMs) for personalized food recommendations. Our proposed method anchors retrieval in the National Health and Nutrition Examination Survey (NHANES) and the Food Patterns Equivalents Database (FPED). A food-level embedding space is constructed from FPED-derived textual descriptions. For each entity, the system computes baseline HEI scores, retrieves candidate foods for intake recommendations, and estimates the HEI impact of simple substitutions or additions. A constrained RAG pipeline instantiated with a pretrained OpenAI LLM generates personalized recommendations and sources based on nutrient profiles and HEI contributions. The simulation results showed a mean HEI improvement of 6.45, with the proportion of users HEI over 50 increasing from 45.12 to 61.26. Quantile analysis revealed consistent improved shifts across the HEI distribution. Our findings suggest that the proposed LLM-RAG-based AI systems can support more precise, explainable, and personalized nutrition guidance to improve diet quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
