Automated and personalized meal plan generation and relevance scoring using a multi-factor adaptation of the transportation problem

Establishing a healthy lifestyle has become a very important aspect in people's lives. The latter requires maintaining a healthy nutrition by considering the nature and quantity of foods being consumed, allowing to regulate one's intake and consumption of calories and nutrients. As a result, people reach out for nutrition experts which services are costly, time-consuming, and not readily available. While various e-solutions have been developed to perform meal planning, yet most of them lack a completely automated process and require domain expert intervention at different stages of the recommendation process (e.g., identifying macronutrient distribution, providing pre-defined meal plans, or combining recommended foods into meal structures). In addition, most solutions focus on fulfilling the patients' nutrition requirements (in terms of caloric intake and macronutrients) while disregarding other relevant factors such as patient food preferences, food variety, food-meal compatibility, and inter-food compatibility. Hence, there is a need for an automated solution to produce a full-fledged meal plan from scratch, based on a recommended caloric intake and considering multiple factors. In this study, we introduce a novel solution titled MPG for automated Meal Plan Generation recommendations, designed based on an adaptation of the transportation optimization problem to simulate the human thought process involved in generating daily meal plans. MPG allows to: (i) generate plans which fulfill a recommended caloric intake, given a set of available foods, while (ii) personalizing the plans following patient chosen factors (e.g., food preferences, variety, and compatibility), and (iii) evaluating the relevance of the produced plans following patient preferences. We have conducted various experiments involving 9 human testers and 124 meal plans to test the performance of MPG. Results highlight MPG's effectiveness in producing healthy and personalized meal plans while complying with the testers' preferences.

Automated summary

Establishing a healthy lifestyle is crucial, requiring attention to nutrition and calorie consumption, but current solutions are costly and lack personalization. An automated solution based on caloric intake and multiple factors is needed to generate personalized meal plans efficiently and effectively. The novel solution, MPG, effectively produces personalized and healthy meal plans while considering individual preferences.