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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5. He, R.; Wang, J.; Tian, J.; Chu, C.-T.; Mauney, B.; Perisic, I.: Session analysis of people search within a professional social network (2013) 0.19

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