Abstract
In this paper we present a methodology designed to improve the intelligent personalization of news services. Our methodology integrates textual content analysis tasks to achieve an elaborate user model, which represents separately short-term needs and long-term multi-topic interests. The characterization of user’s interests includes his preferences about content, using a wide coverage and non-specific-domain classification of topics, and structure (newspaper sections). The application of implicit feedback allows a proper and dynamic personalization.
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© 2001 Springer-Verlag Berlin Heidelberg
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de Buenaga Rodríguez, M., Maña López, M.J., Díaz Esteban, A., Gervás Gómez-Navarro, P. (2001). A User Model Based on Content Analysis for the Intelligent Personalization of a News Service. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds) User Modeling 2001. UM 2001. Lecture Notes in Computer Science(), vol 2109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44566-8_25
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DOI: https://doi.org/10.1007/3-540-44566-8_25
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