Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions
| dc.creator | Borges, Jose | |
| dc.creator | Levene, Mark | |
| dc.date | 2006-06-28 | |
| dc.date.accessioned | 2026-07-07T07:13:07Z | |
| dc.date.available | 2026-07-07T07:13:07Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/cs/0606115 | |
| dc.identifier | http://arxiv.org/abs/cs/0606115 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/112331 | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Information Retrieval | |
| dc.title | Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions | |
| dc.type | text |