IDEAS home Printed from https://ideas.repec.org/a/igg/jwsr00/v15y2018i3p38-60.html
   My bibliography  Save this article

LocPSORank-Prediction of Ranking of Web Services Using Location-Based Clustering and PSO Algorithm

Author

Listed:
  • V. Mareeswari

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India)

  • E. Sathiyamoorthy

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India)

Abstract

Online communities will provide the trustworthiness of their services and also, recommendation systems to improve the commercial value in this competitive business world. Prediction is the greatest method to get people interested whatever offered. Traditional QoS based prediction approach, predicts the QoS value of web service when the incompletion QoS records. This proposed approach introduced cluster based PSO algorithm, which provides better scalability, simplicity, and efficiency. It uses the density-based clusters based on web service users' location and ranks the web services based on PSO algorithm. Here, top-K users are selecting based on web service preferences and weights are giving for experienced neighbors. To achieve the high-quality outcome of the ranking sequence by the control of fitness function and verified by AP correlation coefficient method. The experimental results discussed how this proposed approach provided better prediction accuracy and compared with other existing approaches.

Suggested Citation

  • V. Mareeswari & E. Sathiyamoorthy, 2018. "LocPSORank-Prediction of Ranking of Web Services Using Location-Based Clustering and PSO Algorithm," International Journal of Web Services Research (IJWSR), IGI Global, vol. 15(3), pages 38-60, July.
  • Handle: RePEc:igg:jwsr00:v:15:y:2018:i:3:p:38-60
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWSR.2018070103
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jwsr00:v:15:y:2018:i:3:p:38-60. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.