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Design of Nonlinear Data-Based Wellness Content Recommendation Algorithm

  • Young-Hwan Jang
  • Seung-Su Yang
  • Hyung-Joon Kim
  • Seok-Cheon Park
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

As IT technology has advanced and people’s interest in wellness has increased, recommendation algorithms are being developed to allow people to use wellness content easily. However, existing recommendation algorithms use data entered by users and content-based filtering to recommend content, making it difficult to recommend areas of interest which change in real time. Therefore, in this paper we propose an algorithm which creates user information based on nonlinear social network data and makes recommendations in real time in order to reflect the user’s recent interests. The test result verified that the proposed algorithm improved accuracy by 31% compared to that of the existing content-based recommendation algorithm.

Keywords

Wellness Non-linear data Content-based filtering Recommendation algorithm Wellness recommend content Text mining 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Young-Hwan Jang
    • 1
  • Seung-Su Yang
    • 1
  • Hyung-Joon Kim
    • 2
  • Seok-Cheon Park
    • 3
  1. 1.Department of IT Convergence EngineeringGachon UniversitySeongnamSouth Korea
  2. 2.College of Economics and Business AdministrationHanbat UniversityDaejeonSouth Korea
  3. 3.Department of Computer EngineeringGachon UniversitySeongnamSouth Korea

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