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Experimental Verification of the Dependence Between the Expected and Observed Visit Rate of Web Pages

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Book cover Advanced Intelligent Computing Theories and Applications (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9227))

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Abstract

This paper is focused on a utilization of the web usage mining and web structure mining methods. We tried to answer the question if the expected visit rate of individual web pages correlates with the observed visit rate of the same web pages. We used web server log files as a data source. We applied several log file pre-processing methods to identify the user sessions on different levels of granularity. We found out that the quality of acquired knowledge about the users’ behaviour depends on the method of the session identification. We have experimentally proved a higher dependence between the observed and expected visit rates of the examined web pages in well-prepared files with identified user sessions. We found out statistically significant differences between PageRank and a real visit rate in the files with application of more advanced methods of session identification.

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Acknowledgments

This paper is supported by the project VEGA 1/0392/13 Modelling of Stakeholders’ Behaviour in Commercial Bank during the Recent Financial Crisis and Expectations of Basel Regulations under Pillar 3- Market Discipline and project KEGA 015UKF-4/2013 Modern computer science – New methods and forms for effective education.

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Authors and Affiliations

  1. Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 74, Nitra, Slovakia

    Jozef Kapusta, Michal Munk & Martin Drlik

Authors
  1. Jozef Kapusta
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  2. Michal Munk
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  3. Martin Drlik
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Correspondence to Jozef Kapusta .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Incheon, Korea (Republic of)

    Kyungsook Han

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Kapusta, J., Munk, M., Drlik, M. (2015). Experimental Verification of the Dependence Between the Expected and Observed Visit Rate of Web Pages. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_66

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  • DOI: https://doi.org/10.1007/978-3-319-22053-6_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22052-9

  • Online ISBN: 978-3-319-22053-6

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