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Knowledge-based dietary nutrition recommendation for obese management

Author

Listed:
  • Hoill Jung

    (Sangji University)

  • Kyungyong Chung

    (Sangji University)

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.

Suggested Citation

  • Hoill Jung & Kyungyong Chung, 2016. "Knowledge-based dietary nutrition recommendation for obese management," Information Technology and Management, Springer, vol. 17(1), pages 29-42, March.
    • Handle: RePEc:spr:infotm:v:17:y:2016:i:1:d:10.1007_s10799-015-0218-4
    DOI: 10.1007/s10799-015-0218-4
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    Cited by:

    1. Bel Hadj Tarek & Ghodbane Adel & Aouadi Sami, 2016. "Business Intelligence Versus Competitive Intelligence in the Case of North African SMEs," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-21, December.
    2. Joo-Chang Kim & Kyungyong Chung, 2020. "Knowledge-based hybrid decision model using neural network for nutrition management," Information Technology and Management, Springer, vol. 21(1), pages 29-39, March.
    3. Yun-Hong Noh & Ji-Yun Seo & Do-Un Jeong, 2020. "Development of a Knowledge Discovery Computing based wearable ECG monitoring system," Information Technology and Management, Springer, vol. 21(4), pages 205-216, December.
    4. Yao Cai & Fei Yu & Manish Kumar & Roderick Gladney & Javed Mostafa, 2022. "Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review," IJERPH, MDPI, vol. 19(22), pages 1-15, November.

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