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Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions
April 2007 (vol. 19 no. 4)
pp. 441-452
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.
Index Terms:
Web mining, navigation, Markov processes, modeling and prediction.
Citation:
Jos? Borges, Mark Levene, "Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions," IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 4, pp. 441-452, April 2007, doi:10.1109/TKDE.2007.1012
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