Learning Readers' News Preferences with Support Vector Machines

Elena Hensinger, Ilias Flaounas, Nello Cristianini, Learning Readers' News Preferences with Support Vector Machines. Proceedings of 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011. ISBN 978-3-642-20266-7, pp. 322–331. April 2011. No electronic version available.


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 a?? by users clicking on them a?? 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.

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