PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Personalizing Web Directories with the aid of Web Usage Data
Dimitris Pierrakos and Georgios Paliouras
IEEE Transactions on Knowledge and Data Engineering 2009.

Abstract

This paper presents a knowledge discovery framework for the construction of Community Web Directories, a concept that we introduced in our recent work, applying personalization to Web directories. In this context, the Web directory is viewed as a thematic hierarchy and personalization is realized by constructing user community models on the basis of usage data. In contrast to most of the work on Web usage mining, the usage data that are analyzed here correspond to user navigation throughout the Web, rather than a particular Web site, exhibiting as a result a high degree of thematic diversity. For modeling the user communities, we introduce a novel methodology that combines the users’ browsing behavior with thematic information from the Web directories. Following this methodology we enhance the clustering and probabilistic approaches presented in previous work and we also present a new algorithm that combines these two approaches. The resulting community models take the form of Community Web Directories. The proposed personalization methodology is evaluated both on a specialized artificial and a general-purpose Web directory, indicating its potential value to the Web user. The experiments also assess the effectiveness of the different machine learning techniques on the task.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Information Retrieval & Textual Information Access
ID Code:6224
Deposited By:Dimitris Pierrakos
Deposited On:08 March 2010