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.
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Zhu, T., Greiner, R., Häubl, G., Price, B. (2005). Predicting Web Information Content. In: Mobasher, B., Anand, S.S. (eds) Intelligent Techniques for Web Personalization. ITWP 2003. Lecture Notes in Computer Science(), vol 3169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11577935_13
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DOI: https://doi.org/10.1007/11577935_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29846-5
Online ISBN: 978-3-540-31655-8
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