| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 1.58 MB | Adobe PDF |
Advisor(s)
Abstract(s)
In this study, we present an advanced recommendation system
specifically engineered to aid nutritionists in developing personalized,
optimized nutritional plans. Our system operates by amassing a
broad range of data including users’ preferences, dietary restrictions, and
specific nutritional requirements, which it then utilizes to craft a diverse
assortment of meal choices individually tailored to each user. A key innovation
of our system is its ability to facilitate continuous diet monitoring,
eliminating the need for repeated consultations to update the nutritional
plans. This allows for real-time dietary adjustments and provides nutritionists
with more accurate data for subsequent plans. Additionally, the
system prioritizes the inclusion of thermogenic foods to maximize nutritional
efficiency, while simultaneously providing a pleasurable experience
for the users. This combination of sophisticated data collection and innovative
food recommendations underscores the potential of our system to
improve the process of nutritional counseling and the generation of nutritional
plans, bringing notable benefits to both practitioners and clients
alike.
Description
Keywords
Recommendation System Thermal-Based Nutritional Plan Food Ranking
Citation
Marcuzzo, Henrique S.; Pereira, Maria J. V.; Alves, Paulo; Foleis, Juliano H. (2024). AquaVitae: Innovating Personalized Meal Recommendations for Enhanced Nutritional Health. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 1, p. 148–161. ISBN 978-3-031-53024-1
Publisher
Springer Nature