On-line adaptive filtering of web pages

Authors:
Richard Nock;Babak Esfandiari
Affiliations:
Grimaag, Université Antilles-Guyane, Schoelcher, France;Dept of Systems and Computer Engineering, Carleton University, Ottawa, Canada
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

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Abstract

We present a browser extension to dynamically learn to filter unwanted Uniform Resource Locators (such as advertisements or flashy images) based on minimal user feedback. Our extension builds upon one of the top ten of Mozilla firefox plug-ins which filters URLs without learning capabilities. We apply a weighted majority-type learning algorithm working on regular expressions. Experimental results confirm that the accuracy of the predictions converges quickly to very high levels, with other key parameters: recall, specificity and precision.