PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Readers' News Preferences with Support Vector Machines
Elena Hensinger, Ilias Flaounas and Nello Cristianini
In: International Conference on Adaptive and Natural Computing Algorithms (ICANNGA'11), 14-16 April 2011, Ljubljana, Slovenia.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:7263
Deposited By:Elena Hensinger
Deposited On:17 March 2011