Predicting web information content

Authors:
Tingshao Zhu;Russ Greiner;Gerald Häubl;Bob Price
Affiliations:
Department of Computing Science, University of Alberta, Canada;Department of Computing Science, University of Alberta, Canada;School of Business, University of Alberta, Canada;Department of Computing Science, University of Alberta, Canada
Venue:
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Year:
2003

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Abstract

This paper introduces a novel method for predicting the current information need of a web user from the content of the pages the user has visited and the actions the user has applied to these pages. This inference is based on a parameterized model of how the sequence of actions chosen by the user indicates the degree to which page content satisfies the user's information need. We show that the model parameters can be estimated using standard methods from a labelled corpus. Data from lab experiments demonstrate that the prediction model can effectively identify the information needs of new users, browsing previously unseen pages. The paper concludes with an overview of our “complete-web” recommendation system, WebIC, which uses the prediction model to recommend useful pages to the user, from anywhere on the Web.