An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations
A new study proposes a framework for personalized food recommendations using a Healthy Eating Index (HEI) informed retrieval-augmented generation (RAG) approach. This method combines standardized nutrition databases with large language models to improve diet quality. The results indicate a significant improvement in HEI scores among users, suggesting the effectiveness of AI in providing tailored nutrition guidance.
- ▪The study introduces a framework that integrates HEI with AI for personalized food recommendations.
- ▪Simulation results showed a mean HEI improvement of 6.45 among users.
- ▪The proportion of users with an HEI over 50 increased from 45.12 to 61.26.
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Computer Science > Information Retrieval arXiv:2605.15213 (cs) [Submitted on 11 May 2026] Title:An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations Authors:Yibin Wang, Yanjie Yang, Grace Melo Guerrero, Rodolfo M. Nayga Jr., Azlan Zahid View a PDF of the paper titled An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations, by Yibin Wang and 4 other authors View PDF Abstract: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.
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