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A Customizable Behavior Model for Temporal Prediction of Web User Sequences

  • 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

One of the important Internet challenges in coming years will be the introduction of intelligent services and the creation of a more personalized environment for users. A key prerequisite for such services is the modeling of user behavior and a natural starting place for this are Web logs. In this paper we propose a model for predicting sequences of user accesses which is distinguished by two elements: it is customizable and it reflects sequentiality. Customizable, in this context, means that the proposed model can be adapted to the characteristics of the server to more accurately capture its behavior. The concept of sequentiality in our model consists of three elements: (1) preservation of the sequence of the click stream in the antecedent, (2) preservation of the sequence of the click stream in the consequent and (3) a measure of the gap between the antecedent and the consequent in terms of the number of user clicks.

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Author information

Authors and Affiliations

  1. Courant Institute of Mathematical Sciences, New York University, 715 Broadway, New York, NY, 10003, USA

    Enrique Frías-Martínez & Vijay Karamcheti

Authors
  1. Enrique Frías-Martínez
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  2. Vijay Karamcheti
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Editor information

Editors and Affiliations

  1. University of Alberta, Canada

    Osmar R. Zaïane

  2. University of Minnesota, Minneapolis, MN, USA

    Jaideep Srivastava

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

    Myra Spiliopoulou

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

    Brij Masand

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© 2003 Springer-Verlag Berlin Heidelberg

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Frías-Martínez, E., Karamcheti, V. (2003). A Customizable Behavior Model for Temporal Prediction of Web User Sequences. 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_5

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  • DOI: https://doi.org/10.1007/978-3-540-39663-5_5

  • 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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