Modeling web navigation using grammatical inference

Korfiatis, G; Paliouras, G

dc.contributor.author	Korfiatis, G	en
dc.contributor.author	Paliouras, G	en
dc.date.accessioned	2014-03-01T01:28:47Z
dc.date.available	2014-03-01T01:28:47Z
dc.date.issued	2008	en
dc.identifier.issn	0883-9514	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18970
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Inference engines	en
dc.subject.other	Information use	en
dc.subject.other	Mathematical models	en
dc.subject.other	Merging	en
dc.subject.other	Web services	en
dc.subject.other	Content-based recommendation	en
dc.subject.other	Grammatical inference	en
dc.subject.other	User modeling	en
dc.subject.other	Web navigation	en
dc.subject.other	Computational grammars	en
dc.title	Modeling web navigation using grammatical inference	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/08839510701853267	en
heal.identifier.secondary	http://dx.doi.org/10.1080/08839510701853267	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	In this article, a method that models user navigation on the web, as opposed to a single website, is presented, aiming to assist the user by recommending pages. User modeling is done through data mining of web usage logs, resulting in aggregate, rather than personal models. The proposed approach extends grammatical inference methods by introducing an extra merging criterion, which examines the semantic similarity of automaton states. The experimental results showed that the method does indeed facilitate the modeling of web navigation, which was not possible with the existing web usage mining methods. However, a content-based recommendation model is shown to still outperform the proposed method, which suggests that the knowledge of the navigation sequence does not contribute to the recommendation process. This is due to the thematic cohesion of navigation sessions, in comparison to the large thematic diversity of web usage data. Among three variants of the proposed method, the one based on Blue Fringe, that examines a larger space of possible merges, performs better.	en
heal.publisher	TAYLOR & FRANCIS INC	en
heal.journalName	Applied Artificial Intelligence	en
dc.identifier.doi	10.1080/08839510701853267	en
dc.identifier.isi	ISI:000253657000006	en
dc.identifier.volume	22	en
dc.identifier.issue	1-2	en
dc.identifier.spage	116	en
dc.identifier.epage	138	en