A Customizable Behavior Model for Temporal Prediction of Web User Sequences

  • Enrique Frías-Martínez
  • Vijay Karamcheti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2703)


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.


Association Rule Prediction System Prediction Rate Frequent User Temporal Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Enrique Frías-Martínez
    • 1
  • Vijay Karamcheti
    • 1
  1. 1.Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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