Learning Readers’ News Preferences with Support Vector Machines

  • Elena Hensinger
  • Ilias Flaounas
  • Nello Cristianini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6594)


We explore the problem of learning and predicting popularity of articles from online news media. The only available information we exploit is the textual content of the articles and the information whether they became popular – by users clicking on them – or not. First we show that this problem cannot be solved satisfactorily in a naive way by modelling it as a binary classification problem. Next, we cast this problem as a ranking task of pairs of popular and non-popular articles and show that this approach can reach accuracy of up to 76%. Finally we show that prediction performance can improve if more content-based features are used. For all experiments, Support Vector Machines approaches are used.


Pattern recognition Data mining Applications Machine learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Elena Hensinger
    • 1
  • Ilias Flaounas
    • 1
  • Nello Cristianini
    • 1
  1. 1.Intelligent Systems LaboratoryUniversity of BristolUK

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