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Adaptive Web Usage Profiling

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Advances in Web Mining and Web Usage Analysis (WebKDD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4198))

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Abstract

Web usage models and profiles capture significant interests and trends from past accesses. They are used to improve user experience, say through recommendation of pages, pre-fetching of pages, etc. While browsing behavior changes dynamically over time, many web usage modeling techniques are static due to prohibitive model compilation times and also lack of fast incremental update mechanism. However, profiles have to be maintained so that they dynamically adapt to new interests and trends, since otherwise their use can lead to poor, irrelevant, and mis-targeted recommendations in personalization systems. We present a new profile maintenance scheme, which extends the Relational Fuzzy Subtractive Clustering (RFSC) technique and enables efficient incremental update of usage profiles. An impact factor is defined whose value can be used to decide the need for recompilation. The results from extensive experiments on a large real dataset of web logs show that the proposed maintenance technique, with considerably reduced computational costs, is almost as good as complete remodeling.

This work is an extended version of our earlier work presented at WebKDD 2005 [29].

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Authors and Affiliations

  1. Dept. of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada

    Bhushan Shankar Suryavanshi, Nematollaah Shiri & Sudhir P. Mudur

Authors
  1. Bhushan Shankar Suryavanshi
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  2. Nematollaah Shiri
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  3. Sudhir P. Mudur
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Editor information

Editors and Affiliations

  1. Knowledge Discovery & Web Mining Lab, University of Louisville, 40292, Louisville, KY, USA

    Olfa Nasraoui

  2. University of Alberta, Canada

    Osmar Zaïane

  3. Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

    Myra Spiliopoulou

  4. Center for Web Intelligence School of Computing, DePaul University, Chicago, Illinois, USA

    Bamshad Mobasher

  5. Data Miners Inc., 77 North Washington Street, 02114, Boston, MA, USA

    Brij Masand

  6. University of Illinois, Chicago, IL, USA

    Philip S. Yu

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© 2006 Springer-Verlag Berlin Heidelberg

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Suryavanshi, B.S., Shiri, N., Mudur, S.P. (2006). Adaptive Web Usage Profiling. In: Nasraoui, O., Zaïane, O., Spiliopoulou, M., Mobasher, B., Masand, B., Yu, P.S. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2005. Lecture Notes in Computer Science(), vol 4198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11891321_7

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  • DOI: https://doi.org/10.1007/11891321_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46346-7

  • Online ISBN: 978-3-540-46348-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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