Food KBQA Recommender: A knowledge base question answering system powered meal plan recommender

International audience ; This study investigates the efficacy of a hybrid recommendation model for personalized meal plans, integrating Knowledge Base Question Answering (KBQA), Information Retrieval (IR), and Recommendation techniques. It utilizes the hybrid model to consider both different dietary preferences and nutritional requirements. In addition, it tries to bridge the gap between the recommender itself and its effectiveness in the real world by offering interfaces for integrating persuasion via explanation and gamification.The findings contribute to extending knowledge about the development of food recommendation systems in constrained contexts. The system can address health awareness by considering user-defined constraints. However, in big use cases, it has issues with its scalability. Future work involves refining the data generation processes and exploring non-KBQA models for broader scalability and adaptability.