Adaptive filtering of advertisements on web pages

Authors:
Babak Esfandiari;Richard Nock
Affiliations:
Carleton University, Ottawa, ON, Canada;Grimaag-DSI / Univ. Antilles-Guyane, Cedex, Martinique, France
Venue:
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Year:
2005

Citing 2
Cited 1

Collaborative interface agents

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Machine Learning

Machine Learning

Ads-portal domains: Identification and measurements

ACM Transactions on the Web (TWEB)

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Abstract

We present a browser extension to dynamically learn to filter unwanted images (such as advertisements or flashy graphics) based on minimal user feedback. To do so, we apply the weighted majority algorithm using pieces of the Uniform Resource Locators of such images as predictors. Experimental results tend to confirm that the accuracy of the predictions converges quickly to very high levels.