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2005
ACM

Adaptive filtering of advertisements on web pages

9 years 10 months ago
Adaptive filtering of advertisements on web pages
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. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning; I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence--Intelligent Agents; H.4.3 [Information Systems Applications]: Communications Applications--Information Browsers General Terms Algorithms, Theory, Experimentation Keywords Advertisement Filtering, Weighted Majority, Interface Agents
Babak Esfandiari, Richard Nock
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2005
Where WWW
Authors Babak Esfandiari, Richard Nock
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