Improving revisitation browsers capability by using a dynamic bookmarks personal toolbar

Authors:
José A. Gámez;Juan L. Mateo;José M. Puerta
Affiliations:
Computing Systems Department, Intelligent Systems and Data Mining Group, University of Castilla-La Mancha, Albacete, Spain;Computing Systems Department, Intelligent Systems and Data Mining Group, University of Castilla-La Mancha, Albacete, Spain;Computing Systems Department, Intelligent Systems and Data Mining Group, University of Castilla-La Mancha, Albacete, Spain
Venue:
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Year:
2007

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Abstract

In this paper we present a new approach to add intelligence to Internet browsers user interface. Our contribution is based on improving browsers revisitation capabilities by learning a model from user's navigation behaviour, that later is used to predict a set of bookmarks likely to be used next. These set of bookmarks must be a list of moderate size (≥ 10) because our goal is to show them in the browser bookmarks personal toolbar. We think that dealing with this part of the user interface is beneficial for revisitation because it is always visible and on the contrary to history or bookmarks list (tree) the user can access the desired web page by using a single mouse click. In this work we focus on performing the comparison of several (computationally) simple classifiers in order to identify a good candidate to be used as user navigation model. From the experiments carried out we identify that a combination of Naive Bayes with OneR could be a good choice.