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.
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Yamakami, T. (2008). Intraday-scale Long Interval Method of Classifying Intramonth-Scale Revisiting Mobile Users. In: Oya, M., Uda, R., Yasunobu, C. (eds) Towards Sustainable Society on Ubiquitous Networks. IFIP – The International Federation for Information Processing, vol 286. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85691-9_3
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DOI: https://doi.org/10.1007/978-0-387-85691-9_3
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