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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2007

Autores

José Luís Moura Borges (Autor) FEUP; Levene, M (Autor) Outra

Revista

Título: IEEE Transactions on Knowledge and Data Engineering

Vol. 19 Nº 4

Páginas: 441-452

ISSN: 1041-4347

Editora: IEEE

Indexação

ISI Web of Knowledge - 45 Citações

Scopus - 77 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12