Food recommender systems for diabetic patients: a Narrative review

Document Type: Review

Authors

1 Department of Medical Informatics, Faculty of medicine, Mashhad University of Medical Sciences, Mashhad, Iran

2 Department of Nutrition, Faculty of medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

WHO estimates that the number of people with diabetes will grow 114% by 2030.It declares that, patients have to play a major role to control and therapy of diabetes by being provided with updated knowledge about the disease and different aspects of available treatments, diet therapy in particular. In this regard, diets recommender Systems would be helpful. They are techniques and tools which suggest the best diets according to patient's health situation and preferences. Accordingly this narrative reviewed studies on the topic of food recommender systems and their features by focusing on nutrition and diabetic issues. Literature searches whit Google scholar and Pubmed were conducted during June and October 2014 and February 2015. Results were limited to papers in English and no limits were applied for the published year.  We recognize three common methods for food recommender system: collaborative filtering recommender system (CFRS), knowledge based recommender system (KBRS) and context-aware recommender system (CARS). Also wellness recommender systems are a subfield of food recommender systems which help users to find and adapt suitable personalized wellness treatments based on their individual needs.  Food recommender systems often used artificial intelligence and semantic web techniques. Some used the combination of both techniques

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