Improving of intelligent decision support systems for planning a balanced diet

The object of this study is the process of creating a personalized menu. The subject of the study is recommendation systems for generating breakfast, lunch, snack, and dinner menus. The task solved was the development of an effective system for supporting the decisions by a wide range of users in planning a balanced diet. To form a menu of dishes of different categories of meals in a hybrid system for planning a balanced human diet, it is proposed to use different recommendation systems based on different models of artificial intelligence. The choice of the singular matrix decomposition model, the gradient boosting model of decision trees, and the wide and deep learning models for recommendation systems for forming a menu of dishes has been substantiated by the results of analysis. Based on the results of the experiment with these artificial intelligence models, it was determined which of them are more effective in solving the problem of forming a menu of meals for different categories of meals. The effectiveness of all models was evaluated by such test indicators as Precision@K, mean absolute and root mean square error. The feasibility of choosing the singular matrix decomposition model for generating breakfast menus and the wide and deep learning models for generating snack, lunch, and dinner menus was evaluated by the Precision@K values. The singular matrix decomposition model, compared to the other models studied in this paper, showed the highest Precision@K for breakfast, namely 0.942. The wide and deep learning models demonstrated the highest Precision@K for lunch, snack, and dinner: 0.961, 0.977, and 0.951, respectively. In practice, the results could be used to develop highly efficient personalized meal planning services in mobile and online platforms