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Advisor(s)
Abstract(s)
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.
Description
Keywords
Food recommendation Deep learning Autonomous nutrition
Citation
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