Knowledge-based dietary nutrition recommendation for obese management

Hoill Jung () and Kyungyong Chung ()
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Hoill Jung: Sangji University
Kyungyong Chung: Sangji University

Information Technology and Management, 2016, vol. 17, issue 1, No 4, 29-42

Abstract: Abstract As the basic paradigm of health management has changed from diagnosis and treatment to preventative management, health improvement and management has received growing attention in societies around the world. Recently the number of obese youth has risen globally and obesity has caused serious problems regarding almost all of the diseases of these days. This study presents dietary nutrition recommendations based on knowledge for obese youth. The knowledge-based dietary nutrition recommendations herein include not only static dietary nutritional data but also individualized diet menus for them by utilizing knowledge-based context data through a collaborative filtering method. The suggested method utilizes the basic information on obese youth, forms a similarity clustering with a high correlation, applies the similarity weight on {user-menu} matrix within the similarity clustering and utilizes the knowledge based collaborative filtering to recommend the dietary nutritional menu. Also by using the knowledge-based context-aware modeling, the study constitutes a {user-menu} merge matrix and solves the sparse problem of previous recommendation system. The suggested method herein, unlike the conventional uniformed dietary nutrition recommendations for obesity management, is capable of providing the personalized recommendations. Also through mobile devices, users can receive personalized recipes and menus anytime and anywhere. By using the proposed method, the researcher develops a mobile application of dietary nutrition recommendation service for obese management. A mobile interface will be built herein and applied in an experiment to test its logical validity and effectiveness.

Keywords: Data mining; Knowledge-based; Obese management; Obesity; Dietary nutrition; Recommendation; U-healthcare; IT convergence (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10799-015-0218-4

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