IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v17y2016i1d10.1007_s10799-015-0218-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-015-0218-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10799-015-0218-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infotm:v:17:y:2016:i:1:d:10.1007_s10799-015-0218-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.