Dynamic web log session identification with statistical language models

  • Authors:

  • Xiangji Huang;Fuchun Peng;Aijun An;Dale Schuurmans

  • Affiliations:

  • School of Analytical Studies and Information Technology, York University, Toronto, Ontario, Canada M3J 1P3;Center for Intelligent Information Retrieval, Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, York University, Toronto, Ontario, Canada M3J 1P3;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada T6G 2E8

  • Venue:
  • Journal of the American Society for Information Science and Technology - Special issue: Webometrics
  • Year:
  • 2004

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Abstract

We present a novel session identification method based on statistical language modeling. Unlike standard time-out methods, which use fixed time thresholds for session identification, we use an information theoretic approach that yields more robust results for identifying session boundaries. We evaluate our new approach by learning interesting association rules from the segmented session files. We then compare the performance of our approach to three standard session identification method--the standard timeout method, the reference length method, and the maximal forward reference method--and find that our statistical language modeling approach generally yields superior results. However, as with every method, the performance of our technique varies with changing parameter settings. Therefore, we also analyze the influence of the two key factors in our language-modeling-based approach: the choice of smoothing technique and the language model order. We find that all standard smoothing techniques, save one, perform well, and that performance is robust to language model order.