IJMLC 2014 Vol.4(2): 139-141 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.401

Predicting Future Citation Count Using Bibliographic and Author Information of Articles

Takashi Matsui, Katsutoshi Kanamori, and Hayato Ohwada

Abstract—Electronic journal continues to spread rapidly among academic researchers. Citation count shown of an article in academic journals plays an important role in academia. It helps academic researchers in choosing suitable and reliable references for their research. It is unfortunate that recently published papers tend to be unapparent, since it has less citation count than other former works. This research proposes a new way to predict future citation count of an article using regression analysis by machine learning. As a result, citation count prediction of article is delivered using the new proposed method.

Index Terms—Citation count, article, regression analysis, prediction.

Takashi Matsui, Katsutoshi Kanamori, and Hayato Ohwada are with the Tokyo University of Science, Noda-shi, Chiba-ken, Japan (e-mail: t-matsui@ohwada-lab.net, katsu@rs.tus.ac.jp, ohwada@rs.tus.ac.jp).

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Cite: Takashi Matsui, Katsutoshi Kanamori, and Hayato Ohwada, "Predicting Future Citation Count Using Bibliographic and Author Information of Articles," International Journal of Machine Learning and Computing vol.4, no. 2, pp. 139-141, 2014.

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: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net