Functional Food Knowledge Graph-based Recipe Recommendation System Focused on Lifestyle-Related Diseases
Akio Kobayashi, Shotaro Mori, Akira Hashimoto, Tetsuo Katsuragi, Takahiro Kawamura · 2024
Lifestyle-related diseases can be reduced by making daily dietary choices. Functional components in foods have the potential to provide benefits in this regard. We constructed a knowledge graph that connects the functional components of foods to recipes, and developed a recommendation system to suggest dishes that may help alleviate lifestyle-related diseases. As dietary requirements vary widely depending on specific diseases and individual conditions, these requirements are combined to form a vast probability distribution. Our proposed system uses probabilistic logic programming to generate recommendations that are based on disease-specific dietary requirements, using a knowledge graph of foods and information about the user’s condition. In the proposed method, nodes in the knowledge graph, such as food functionality or recipes, are characterized by their relationships with other surrounding nodes, such as ingredients. We conducted an experiment to suggest recipes specifically for patients with diabetes and dyslipidemia. At the result, our system enable to recommend numerous recipes beneficial for the improvement of lifestyle-related diseases, especially for users with diabetes, with a precision of over 0.99.