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2015 | OriginalPaper | Buchkapitel

Identification of Underestimated and Overestimated Web Pages Using PageRank and Web Usage Mining Methods

verfasst von : Jozef Kapusta, Michal Munk, Martin Drlík

Erschienen in: Transactions on Computational Collective Intelligence XVIII

Verlag: Springer Berlin Heidelberg

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Abstract

The paper describes an alternative method of website analysis and optimization that combines methods of web usage and web structure mining - discovering of web users’ behaviour patterns as well as discovering knowledge from the website structure. Its primary objective is identifying of web pages, in which the value of their importance, estimated by the website developers, does not correspond to the real behaviour of the website visitors. It was proved before that the expected visit rate correlate with the observed visit rate of the web pages. Consequently, the expected probabilities of visiting of web pages by a visitor were calculated using the PageRank method and observed probabilities were obtained from the web server log files using the web usage mining method. The observed and expected probabilities were compared using the residual analysis. While the sequence rules analysis can only uncover the potential problem of web pages with higher visit rate, the proposed method of residual analysis can also consider other web pages with a smaller visit rate. The obtained results can be successfully used for a website optimization and restructuring, improving website navigation, and adaptive website realisation.

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Titel: Identification of Underestimated and Overestimated Web Pages Using PageRank and Web Usage Mining Methods
verfasst von: Jozef Kapusta
Michal Munk
Martin Drlík
Verlag: Springer Berlin Heidelberg
Buch: Transactions on Computational Collective Intelligence XVIII
Print ISBN: 978-3-662-48144-8

Electronic ISBN: 978-3-662-48145-5

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-662-48145-5_7

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