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Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study

This study aimed to predict dietary recommendations and compare the performance of algorithms based on collaborative filtering for making predictions of personalized dietary recommendations. We analyzed the baseline cross-sectional data (2008–2010) of 12,667 participants of the Brazilian Longitudina...

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Autores principales: Silva, Vanderlei Carneiro, Gorgulho, Bartira, Marchioni, Dirce Maria, Alvim, Sheila Maria, Giatti, Luana, de Araujo, Tânia Aparecida, Alonso, Angelica Castilho, Santos, Itamar de Souza, Lotufo, Paulo Andrade, Benseñor, Isabela Martins
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9690822/
https://www.ncbi.nlm.nih.gov/pubmed/36429651
http://dx.doi.org/10.3390/ijerph192214934
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author Silva, Vanderlei Carneiro
Gorgulho, Bartira
Marchioni, Dirce Maria
Alvim, Sheila Maria
Giatti, Luana
de Araujo, Tânia Aparecida
Alonso, Angelica Castilho
Santos, Itamar de Souza
Lotufo, Paulo Andrade
Benseñor, Isabela Martins
author_facet Silva, Vanderlei Carneiro
Gorgulho, Bartira
Marchioni, Dirce Maria
Alvim, Sheila Maria
Giatti, Luana
de Araujo, Tânia Aparecida
Alonso, Angelica Castilho
Santos, Itamar de Souza
Lotufo, Paulo Andrade
Benseñor, Isabela Martins
author_sort Silva, Vanderlei Carneiro
collection PubMed
description This study aimed to predict dietary recommendations and compare the performance of algorithms based on collaborative filtering for making predictions of personalized dietary recommendations. We analyzed the baseline cross-sectional data (2008–2010) of 12,667 participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The participants were public employees of teaching and research institutions, aged 35–74 years, and 59% female. A semiquantitative Food Frequency Questionnaire (FFQ) was used for dietary assessment. The predictions of dietary recommendations were based on two machine learning (ML) algorithms—user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). The ML algorithms had similar precision (88–91%). The error metrics were lower for UBCF than for IBCF: with a root mean square error (RMSE) of 1.49 vs. 1.67 and a mean square error (MSE) of 2.21 vs. 2.78. Although all food groups were used as input in the system, the items eligible as recommendations included whole cereals, tubers and roots, beans and other legumes, oilseeds, fruits, vegetables, white meats and fish, and low-fat dairy products and milk. The algorithms’ performances were similar in making predictions for dietary recommendations. The models presented can provide support for health professionals in interventions that promote healthier habits and improve adherence to this personalized dietary advice.
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spelling pubmed-96908222022-11-25 Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study Silva, Vanderlei Carneiro Gorgulho, Bartira Marchioni, Dirce Maria Alvim, Sheila Maria Giatti, Luana de Araujo, Tânia Aparecida Alonso, Angelica Castilho Santos, Itamar de Souza Lotufo, Paulo Andrade Benseñor, Isabela Martins Int J Environ Res Public Health Article This study aimed to predict dietary recommendations and compare the performance of algorithms based on collaborative filtering for making predictions of personalized dietary recommendations. We analyzed the baseline cross-sectional data (2008–2010) of 12,667 participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The participants were public employees of teaching and research institutions, aged 35–74 years, and 59% female. A semiquantitative Food Frequency Questionnaire (FFQ) was used for dietary assessment. The predictions of dietary recommendations were based on two machine learning (ML) algorithms—user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). The ML algorithms had similar precision (88–91%). The error metrics were lower for UBCF than for IBCF: with a root mean square error (RMSE) of 1.49 vs. 1.67 and a mean square error (MSE) of 2.21 vs. 2.78. Although all food groups were used as input in the system, the items eligible as recommendations included whole cereals, tubers and roots, beans and other legumes, oilseeds, fruits, vegetables, white meats and fish, and low-fat dairy products and milk. The algorithms’ performances were similar in making predictions for dietary recommendations. The models presented can provide support for health professionals in interventions that promote healthier habits and improve adherence to this personalized dietary advice. MDPI 2022-11-13 /pmc/articles/PMC9690822/ /pubmed/36429651 http://dx.doi.org/10.3390/ijerph192214934 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Silva, Vanderlei Carneiro
Gorgulho, Bartira
Marchioni, Dirce Maria
Alvim, Sheila Maria
Giatti, Luana
de Araujo, Tânia Aparecida
Alonso, Angelica Castilho
Santos, Itamar de Souza
Lotufo, Paulo Andrade
Benseñor, Isabela Martins
Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study
title Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study
title_full Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study
title_fullStr Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study
title_full_unstemmed Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study
title_short Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study
title_sort recommender system based on collaborative filtering for personalized dietary advice: a cross-sectional analysis of the elsa-brasil study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9690822/
https://www.ncbi.nlm.nih.gov/pubmed/36429651
http://dx.doi.org/10.3390/ijerph192214934
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