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A Web User Profiling Approach

  • Younes Hafri
  • Chabane Djeraba
  • Peter Stanchev
  • Bruno Bachimont
Conference paper
  • 478 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2642)

Abstract

People display regularities in almost everything they do. This paper proposes characteristics of an idealized algorithm that would allow an automatic extraction of web user profil based on user navigation paths. We describe a simple predictive approach with these characteristics and show its predictive accuracy on a large dataset from KDD-Cup web logs (a commercial web site), while using fewer computational and memory resources. To achieve this objective, our approach is articulated around three notions: (1) Applying probabilistic exploration using Markov models. (2) Avoiding the problem of Markov model high-dimensionality and sparsity by clustering web documents, based on their content, before applying the Markov analysis. (3) Clustering Markov models, and extraction of their gravity centers. On the basis of these three notions, the approach makes possible the prediction of future states to be visited in k steps and navigation sessions monitoring, based on both content and traversed paths.

Keywords

Markov Model Hide Markov Model Speech Recognition Page Number Gravity Center 
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

  • Younes Hafri
    • 1
    • 2
  • Chabane Djeraba
    • 2
  • Peter Stanchev
    • 3
  • Bruno Bachimont
    • 1
  1. 1.Institut National de l’AudiovisuelBry-sur-Marne CedexFrance
  2. 2.Institut de Recherche en Informatique de NantesNantes CedexFrance
  3. 3.Kettering UniversityFlintUSA

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