Abstract
Recommendation systems have played a crucial role in assisting users with decision-making across various domains. In nutrition, these systems can provide valuable assistance by offering alternatives to inflexible food plans that often result in abandonment due to personal food preferences or the temporary unavailability of certain ingredients. Moreover, they can aid caregivers in selecting the most suitable food options for dependent individuals based on their specific daily goals. In this article, we develop a data-driven model using a multilayer perceptron (MLP) network to assist individuals in making informed meal choices that align with their preferences and daily goals. Our study focuses on predicting complete meals rather than solely on predicting individual food items since food choices are often influenced by specific combinations of ingredients that work harmoniously together. Based on our evaluation of a comprehensive dataset, the results of our study demonstrate that the model achieves a prediction accuracy of over 60% for an individual complete meal.
National Funds fund this work through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Ref. UIDB/05583/2020. Furthermore, we thank the Research Centre in Digital Services (CISeD), under project Ref. PIDI/CISeD/2022/009, and the Polytechnic of Viseu for their support.
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Cunha, C.A.S., Cardoso, T.R., Duarte, R.P. (2024). Meal Suggestions for Caregivers and Indecisive Individuals Without a Set Food Plan. In: Coelho, P.J., Pires, I.M., Lopes, N.V. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 556. Springer, Cham. https://doi.org/10.1007/978-3-031-52524-7_13
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