Automatic removal of advertising from web-page display
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Learning block importance models for web pages
Proceedings of the 13th international conference on World Wide Web
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Blocking online advertising - A state of the art
ICIT '09 Proceedings of the 2009 IEEE International Conference on Industrial Technology
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Web advertising has become a major industry and a large part of this market consists of contextual ads. Although it has made a great impact on earnings of many publishers' websites, these advertisements tend to disturb the internet surfing of normal users and to consume a lot of valuable bandwidth. Moreover, they always bring extra burden in indexing to commercial search engines as they mix up with the main content of the hosting web pages. Therefore, it is necessary to automatically detect those contextual ads on the web. In this paper, a classification based approach is proposed for contextual ads detection. Those features include text, link, layout and style in hosting web pages. Furthermore, neural network is used to identify the parameters that contribute the most in detecting contextual ads from non-contextual ads. Promising experimental results are obtained on ATOM textual snippets collected from 219 web sites.