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Integrating Multilingual Text Classification Tasks and User Modeling in Personalized Newspaper Services

  • Alberto Díaz Esteban
Conference paper
  • 690 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2109)

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.

Keywords

Short/long-term models multi-topic user profile adaptive user model evaluation multilingual text classification tasks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alberto Díaz Esteban
    • 1
  1. 1.Departamento de Inteligencia ArtificialUniversidad EuropeaMadridSpain

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