PNRG: Knowledge Graph-Driven Methodology for Personalized Nutritional Recommendation Generation

Abstract Chronic Diseases are a prevalent problem that affects millions of people worldwide. It is health condition requires careful diet and medication management preventing chronic diseases. Traditional approaches to nutritional recommendation generation often rely on generic guidelines population-based data, which may not account for individual dietary needs preferences variations. In this paper, we propose knowledge graph driven methodology generating highly personalized recommendations leverage the power graphs integrate analyze complex data about an individual's health, lifestyle, habits. Our employs multi-step process includes collection curation, construction, generation. We illustrate effectiveness our approach through case study in generate sample based their specific goals.