Evaluating variable-length Markov chain models for analysis of user web navigation sessions

de Moura Borges, J.L.C. and Levene, Mark (2007) Evaluating variable-length Markov chain models for analysis of user web navigation sessions. IEEE Transactions on Knowledge and Data Engineering 19 (4), pp. 441-452. ISSN 1041-4347.

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Official URL: https://doi.org/10.1109/TKDE.2007.1012

Abstract

Markov models have been widely used to represent and analyze user Web navigation data. In previous work, we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable-length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable-length Markov model to summarize user Web navigation sessions up to a given length. Although the summarization ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalize a Web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarization ability.

Metadata

Item Type:	Article
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	25 May 2021 18:11
Last Modified:	09 Aug 2023 12:50
URI:	https://eprints.bbk.ac.uk/id/eprint/44421

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