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- W2950482443 abstract "Markov models have been widely used to represent and analyse 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 summarise user web navigation sessions up to a given length. While the summarisation 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 personalise a web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarisation ability." @default.
- W2950482443 created "2019-06-27" @default.
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- W2950482443 date "2006-06-28" @default.
- W2950482443 modified "2023-09-27" @default.
- W2950482443 title "Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions" @default.
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