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Intraday-scale Long Interval Method of Classifying Intramonth-Scale Revisiting Mobile Users

  • Toshihiko Yamakami
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 286)

Abstract

Penetration of the mobile Internet has increased its visibility worldwide. This enables analysis of detailed time-dimensional user behavior data. It also increases the industry need to identify and retain mobile users with strong loyalty to a particular mobile Web site. The author proposes an intramonth-scale revisit classification method for identifying intramonth-scale, revisiting mobile users. The author performs a case study and the result shows that the proposed method shows 87 % classifier accuracy. The author discusses a trade-off between classifier accuracy and a true positive ratio.

Keywords

Mobile User Content Provider Interval Method Mobile Data Service Navigation Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© International Federation for Information Processing 2008

Authors and Affiliations

  • Toshihiko Yamakami
    • 1
  1. 1.ACCESSTokyoJapan

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