Abstract
Part of the world’s population is nutrient deficient, a phenomenon known as hidden hunger. Poor eating conditions cause this deficiency, leading to illnesses and recovery difficulties. Malnourished patients are more easily affected by Covid-19 and have a difficult recovery after the illness. An effective food choice has the price and nutritional value of food products as the most relevant factors, with the price being the most relevant, considering the context of countries such as Brazil. Thus, having identified a scenario in which the access and food price mainly cause malnutrition. This work proposes an architecture, called Nutri’n Price, to recommend high nutritional foods with low costs. The architecture encompasses a network of ontologies, inference algorithms, information retrieval and collaborative filtering techniques to recommend the best foods according to nutrient choice, price, and user contextual information. A prototype of a mobile application was developed to evaluate the feasibility of the proposed architecture.
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Notes
- 1.
Embrapa Gado de Leite, https://www.embrapa.br.
- 2.
Human Disease Ontology, https://disease-ontology.org/.
References
Baccarin, J.G., de Oliveira, J.A.: Inflação de alimentos no brasil em período da pandemia da covid 19, continuidade e mudanças. Segurança Alimentar e Nutricional 28, e021002–e021002 (2021)
Diniz, D.M., Costa, Y.D.S., da Silva, A.M., de Andrade Aoyama, E.: Comprometimento do estado nutricional em pacientes com covid-19. Revista Brasileira Interdisciplinar de Saúde (2021)
Elali, R.: An intention mining approach using ontology for contextual recommendations. In: Krogstie, J., Ouyang, C., Ralyté, J. (eds.) Proceedings of the Doctoral Consortium Papers Presented at the 33rd International Conference on Advanced Information Systems Engineering (CAiSE 2021), Melbourne, Australia, June 28 - July 2, 2021. CEUR Workshop Proceedings, vol. 2906, pp. 69–78. CEUR-WS.org (2021)
Emadi, M.H., Rahmanian, M.: Commentary on challenges to taking a food systems approach within the food and agriculture organization (FAO). In: Squires, V.R., Gaur, M.K. (eds.) Food Security and Land Use Change under Conditions of Climatic Variability, pp. 19–31. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36762-6_2
Esteban, A., Zafra, A., Romero, C.: Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowl.-Based Syst. 194, 105385 (2020)
Gunawardena, D., Sarathchandra, K.: Bestdish: A digital menu and food item recommendation system for restaurants in the hotel sector. In: 2020 International Conference on Image Processing and Robotics (ICIP), pp. 1–7. IEEE (2020)
Harrington, R.A., Adhikari, V., Rayner, M., Scarborough, P.: Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure. BMJ open 9(6), e026652 (2019)
Padhiar, I., Seneviratne, O., Chari, S., Gruen, D., McGuinness, D.L.: Semantic modeling for food recommendation explanations. In: 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW), pp. 13–19. IEEE (2021)
Siqueira, K.B., et al.: Custo benefício dos nutrientes dos alimentos consumidos no brasil. Ciência & Saúde Coletiva 25, 1129–1135 (2020)
Siqueira, K.B., et al.: Nutrient density and affordability of foods in brazil by food group and degree of processing. Public Health Nutr. 24(14), 4564–4571 (2021)
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Alves, R.d.D. et al. (2022). An Architecture for Food Product Recommendation Focusing on Nutrients and Price. In: De Weerdt, J., Polyvyanyy, A. (eds) Intelligent Information Systems. CAiSE 2022. Lecture Notes in Business Information Processing, vol 452. Springer, Cham. https://doi.org/10.1007/978-3-031-07481-3_1
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