Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions
<|reference_start|>Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions: 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.<|reference_end|>
@article{borges2006evaluating,
    title={Evaluating Variable Length Markov Chain Models for Analysis of User Web
  Navigation Sessions},
    author={Jose Borges and Mark Levene},
    journal={arXiv preprint arXiv:cs/0606115},
    year={2006},
    archivePrefix={arXiv},
    eprint={cs/0606115},
    primaryClass={cs.AI cs.IR}
}