Evaluating variable-length Markov chain models for analysis of user Web navigation sessions

• Type: Article in International Scientific Journal
• Date: 2007

Authors

José Luís Moura Borges (Author) FEUP; Levene, M (Author) Other

Journal

Title: IEEE Transactions on Knowledge and Data Engineering

Vol. 19 No. 4

Pages: 441-452

ISSN: 1041-4347

Publisher: IEEE

Indexing

ISI Web of Knowledge - 45 Citations

Scopus - 77 Citations

Other information

Authenticus ID: P-004-AWP

DOI: 10.1109/tkde.2007.1012

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12