Food recommendation systems are increasingly being used by online food services to make recommendations. Health factors are often ignored in most of these systems, despite the fact that unhealthy diets are connected to a wide range of non-communicable diseases. Furthermore, if users do not receive compelling explanations about the recommended healthy foods, they may become hesitant to try them. In this paper, a novel explainable and health-aware food recommender system is developed to address these challenges. For this purpose, user’s preferences and food health factors are taken into account simultaneously and then a rule-based mechanism is employed for final healthy and explainable recommendations. Five performance metrics were used to compare our system with different new recommender systems. Using a dataset crawled from ”Allrecipes.com”, the proposed model is shown to perform best.
