Advertisement
Published in:
2011 | OriginalPaper | Chapter
Learning Readers’ News Preferences with Support Vector Machines
Authors : Elena Hensinger, Ilias Flaounas, Nello Cristianini
Published in: Adaptive and Natural Computing Algorithms
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.