 Dynamic Web log session identification with statistical language modelsXiangji Huang, Fuchun Peng, Aijun An and Dale Schuurmans Journal of the American Society for Information Science and Technology, 2004, vol. 55, issue 14, 1290-1303 Abstract: We present a novel session identification method based on 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 well, and that performance is robust to language model order. Date: 2004 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:55:y:2004:i:14:p:1290-1303 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Journal of the American Society for Information Science and Technology from Association for Information Science & Technology |
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