Systematic Review of Nutritional Recommendation Systems

In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes,...

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Bibliographic Details
Published in:Applied sciences 2021-12, Vol.11 (24), p.12069
Main Authors: Orue-Saiz, Iñigo, Kazarez, Miguel, Mendez-Zorrilla, Amaia
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Calories
Diet
Habits
m-health
Machine learning
Nutrients
nutrition
Nutrition research
recommendation systems
Recommender systems
Systematic review
Weight control
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Summary:In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.
ISSN:2076-3417
2076-3417
DOI:10.3390/app112412069