Statistical machine learning for tracking hypermedia user behaviour

Abstract : We consider the classification and tracking of user navigation patterns for closed world hypermedia. We use a number of statistical machine learning models and compare them on different instances of the classification/tracking problem using a home made access log database. We conclude on the potential and limitations of these methods for user behavior identification and tracking.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01533380
Contributor : Lip6 Publications <>
Submitted on : Tuesday, June 6, 2017 - 1:45:08 PM
Last modification on : Thursday, March 21, 2019 - 2:16:07 PM

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  • HAL Id : hal-01533380, version 1

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Sylvain Bidel, Laurent Lemoine, Frédéric Piat, Thierry Artières, Patrick Gallinari. Statistical machine learning for tracking hypermedia user behaviour. MLIRUM'03 - 2nd Workshop on Machine Learning, Information Retrieval and User Modeling, Jun 2003, Pittsburgh, PA, United States. ⟨hal-01533380⟩

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