Abstract
In this paper a methodology designed to improve the intelligent personalization of newspaper services is presented. The 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 structure, content and information delivery. A wide coverage and non-specific-domain classification of topics and a personal set of keywords allow the user to define his preferences about content. The application of implicit feedback allows a proper and dynamic personalization. Another topic that have been addressed in the thesis is the evaluation of systems offering to send users a selection of the daily news by electronic mail. Finally, the extensions to a multilingual framework are studied.
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Díaz Esteban, A. (2001). Integrating Multilingual Text Classification Tasks and User Modeling in Personalized Newspaper Services. 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_41
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DOI: https://doi.org/10.1007/3-540-44566-8_41
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