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NewsRec, a Personal Recommendation System for News Websites

  • Conference paper
Classification — the Ubiquitous Challenge

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Abstract

The behavior of how individuals select and read news depends on the underlying media for reproduction. Today, the use of news websites is increasing. Online readers usually have to click on abstracts or headlines in order to see full articles. This kind of selection of information is less pleasant than in traditional newspapers where glancing over the whole layout of double pages is possible. Personalization is a possible solution for this article selection problem. So far, most types of personalization are controlled by website owners. In our work, we discuss design aspects and empirical results of our personal recommendation system for news websites, which uses text classification techniques.

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Author information

Authors and Affiliations

  1. Institut für Entscheidungstheorie und Unternehmensforschung, Universität Karlsruhe (TH), 76128, Karlsruhe, Germany

    Christian Bomhardt & Wolfgang Gaul

Authors
  1. Christian Bomhardt
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  2. Wolfgang Gaul
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Editor information

Editors and Affiliations

  1. Fachbereich Statistik, Universität Dortmund, 44221, Dortmund

    Claus Weihs

  2. Institut für Entscheidungstheorie und Unternehmensforschung, Universität Karlsruhe (TH), 76128, Karlsruhe

    Wolfgang Gaul

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© 2005 Springer-Verlag Berlin · Heidelberg

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Bomhardt, C., Gaul, W. (2005). NewsRec, a Personal Recommendation System for News Websites. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_45

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