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

Vanderlei Carneiro Silva (), Bartira Gorgulho, Dirce Maria Marchioni, Sheila Maria Alvim, Luana Giatti, Tânia Aparecida de Araujo, Angelica Castilho Alonso, Itamar de Souza Santos, Paulo Andrade Lotufo and Isabela Martins Benseñor
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Vanderlei Carneiro Silva: Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo 01246-904, Brazil
Bartira Gorgulho: Department of Food and Nutrition, School of Nutrition, Federal University of Mato Grosso, Cuiaba 78060-900, Brazil
Dirce Maria Marchioni: Department of Nutrition, School of Public Health, University of São Paulo, São Paulo 01246-904, Brazil
Sheila Maria Alvim: Institute of Collective Health, Federal University of Bahia, Salvador 40110-040, Brazil
Luana Giatti: Department of Social and Preventive Medicine, Faculty of Medicine & Clinical Hospital, Federal University of Minas Gerais, Belo Horizonte 30130-100, Brazil
Tânia Aparecida de Araujo: Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo 01246-904, Brazil
Angelica Castilho Alonso: Laboratory of the Study of Movement, Faculty of Medicine, University of São Paulo, São Paulo 05403-010, Brazil
Itamar de Souza Santos: Center of Clinical and Epidemiological Research, University Hospital, University of São Paulo, São Paulo 05508-000, Brazil
Paulo Andrade Lotufo: Center of Clinical and Epidemiological Research, University Hospital, University of São Paulo, São Paulo 05508-000, Brazil
Isabela Martins Benseñor: Center of Clinical and Epidemiological Research, University Hospital, University of São Paulo, São Paulo 05508-000, Brazil

IJERPH, 2022, vol. 19, issue 22, 1-12

Abstract: 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.

Keywords: recommender system; collaborative filtering; diet; dietary advice; algorithms (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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