Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement

Abstract

Diet planning is a basic and regular human activity. Previous studies have considered diet planning a combinatorial optimization problem to generate solutions that satisfy a diet's nutritional requirements. However, this approach does not consider the composition of diets, which is critical for diet recipients' to accept and enjoy menus with high nutritional quality. Without this consideration, feasible solutions for diet planning could not be provided in practice. This suggests the necessity of diet planning with machine learning, which extracts implicit composition patterns from real diet data and applies these patterns when generating diets. This work is original research that defines diet planning as a machine learning problem; we describe diets as sequence data and solve a controllable sequence generation problem. Specifically, we develop the Teacher-forced REINFORCE algorithm to connect neural machine translation and reinforcement learning for composition compliance with nutrition enhancement in diet generation. Through a real-world application to diet planning for children, we validated the superiority of our work over the traditional combinatorial optimization and modern machine learning approaches, as well as human (i.e., professional dietitians) performance. In addition, we construct and open the databases of menus and diets to motivate and promote further research and development of diet planning with machine learning. We believe this work with data science will contribute to solving economic and social problems associated with diet planning.