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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9690822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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