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The Impact of Site Structure and User Environment on Session Reconstruction in Web Usage Analysis

  • Conference paper
WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles (WebKDD 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2703))

Abstract

The analysis of user behavior on the Web presupposes a reliable reconstruction of the users’ navigational activities. Cookies and server-generated session identifiers have been designed to allow an accurate session reconstruction. However, in the absence of reliable methods, analysts must employ heuristics (a) to identify unique visitors to a site, and (b) to distinguish among the activities of such users during independent sessions. The characteristics of the site, such as the site structure, as well as the methods used for data collection (e.g., the existence of cookies and reliable synchronization across multiple servers) may necessitate the use of different types of heuristics. In this study, we extend our work on the reliability of sessionizing mechanisms, by investigating the impact of site structure on the quality of constructed sessions. Specifically, we juxtapose sessionizing on a frame-based and a frame-free version of a site. We investigate the behavior of cookies, server-generated session identification, and heuristics that exploit session duration, page stay time and page linkage. Different measures of session reconstruction quality, as well as experiments on the impact on the prediction of frequent entry and exit pages, show that different reconstruction heuristics can be recommended depending on the characteristics of the site. We also present first results on the impact of session reconstruction heuristics on predictive applications such as Web personalization.

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Berendt, B., Mobasher, B., Nakagawa, M., Spiliopoulou, M. (2003). The Impact of Site Structure and User Environment on Session Reconstruction in Web Usage Analysis. In: Zaïane, O.R., Srivastava, J., Spiliopoulou, M., Masand, B. (eds) WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles. WebKDD 2002. Lecture Notes in Computer Science(), vol 2703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39663-5_10

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  • DOI: https://doi.org/10.1007/978-3-540-39663-5_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20304-9

  • Online ISBN: 978-3-540-39663-5

  • eBook Packages: Springer Book Archive

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