An explainable graph retrieval augmented generation framework for personalized nutrition recommendation
Ilanthenral Kandasamy
IntroductionDietary planning is essential for managing non-communicable diseases, yet many AI-based nutrition systems lack structured knowledge grounding, demographic sensitivity, and explainability. These limitations are particularly evident in culturally diverse contexts such as India, where standard approaches often fail to align clinical dietary requirements with traditional meal patterns.MethodsThis study proposes a graph-centric decision-support framework using a Graph Retrieval-Augmented