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IJMLC 2019 Vol.9(5): 682-687 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.858

Evaluation of Credibility for Reviewers and Review Scores Based on Link Analysis

Yasuyuki Shirai

Abstract—In recent years, there are widely used online stores on the Internet which have customer review facilities. Although these reviews help users decide their actions, there are incredible, fake or irresponsible reviews in general. It is difficult for site users and site managers to evaluate the credibility of reviews and reviewers. In this paper, we show the method to quantify the credibility of review scores and to find out credible reviewers and credible review scores based on a bipartite relation between reviewers and sale items. We also show the experimental results on the travel website. Through our experiments, we discuss these indexes for reviewers and sale items are also effective for the future prediction of review scores.

Index Terms—Credibility, user review, travel website, HITS algorithm.

The author is with the Faculty of Business Administration, Daito Bunka University, Tokyo, Japan (e-mail: yasuyuki.shirai@gmail.com).


Cite: Yasuyuki Shirai, "Evaluation of Credibility for Reviewers and Review Scores Based on Link Analysis," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 682-687, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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