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Generating dynamic higher-order Markov models in web usage mining

Borges, J. and Levene, Mark (2005) Generating dynamic higher-order Markov models in web usage mining. In: Jorge, A.M. and Torgo, L. and Brazdil, P. and Camacho, R. and Gama, J. (eds.) Knowledge Discovery in Databases: PKDD 2005. Lecture Notes in Computer Science 3721. Berlin, Germany: Springer, pp. 34-45. ISBN 9783540292449.

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Official URL: http://dx.doi.org/10.1007/11564126_9

Abstract

Markov models have been widely used for modelling users’ web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter.

Item Type: Book Section
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Sandra Plummer
Date Deposited: 17 Jan 2006
Last Modified: 13 May 2013 14:43
URI: http://eprints.bbk.ac.uk/id/eprint/295

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