A Systematic Review on Food Recommender Systems for Diabetic Patients

Raciel Yera (), Ahmad A. Alzahrani, Luis Martínez and Rosa M. Rodríguez
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Raciel Yera: Computer Science Department, University of Jaén, 23007 Jaén, Spain
Ahmad A. Alzahrani: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Luis Martínez: Computer Science Department, University of Jaén, 23007 Jaén, Spain
Rosa M. Rodríguez: Computer Science Department, University of Jaén, 23007 Jaén, Spain

IJERPH, 2023, vol. 20, issue 5, 1-24

Abstract: Recommender systems are currently a relevant tool for facilitating access for online users, to information items in search spaces overloaded with possible options. With this goal in mind, they have been used in diverse domains such as e-commerce, e-learning, e-tourism, e-health, etc. Specifically, in the case of the e-health scenario, the computer science community has been focused on building recommender systems tools for supporting personalized nutrition by delivering user-tailored foods and menu recommendations, incorporating the health-aware dimension to a larger or lesser extent. However, it has been also identified the lack of a comprehensive analysis of the recent advances specifically focused on food recommendations for the domain of diabetic patients. This topic is particularly relevant, considering that in 2021 it was estimated that 537 million adults were living with diabetes, being unhealthy diets a major risk factor that leads to such an issue. This paper is centered on presenting a survey of food recommender systems for diabetic patients, supported by the PRISMA 2020 framework, and focused on characterizing the strengths and weaknesses of the research developed in this direction. The paper also introduces future directions that can be followed in the next future, for guaranteeing progress in this necessary research area.

Keywords: food recommendation; diabetes; user preferences; nutritional information (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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