Document (#28097)

Author
Huang, X.
Peng, F,
An, A.
Schuurmans, D.
Title
Dynamic Web log session identification with statistical language models
Source
Journal of the American Society for Information Science and Technology. 55(2004) no.14, S.1290-1303
Year
2004
Abstract
We present a novel session identification method based an statistical language modeling. Unlike standard timeout 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 methods-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 weIl, and that performance is robust to language model order.
Footnote
Beitrag in einem Themenheft über Webometrics
Theme
Internet
Informetrie

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