Learning from multi-topic web documents for contextual advertisement

Contextual advertising on web pages has become very popular recently and it poses its own set of unique text mining challenges. Often advertisers wish to either target (or avoid) some specific content on web pages which may appear only in a small part of the page. Learning for these targeting tasks is difficult since most training pages are multi-topic and need expensive human labeling at the sub-document level for accurate training. In this paper we investigate ways to learn for sub-document classification when only page level labels are available - these labels only indicate if the relevant content exists in the given page or not. We propose the application of multiple-instance learning to this task to improve the effectiveness of traditional methods. We apply sub-document classification to two different problems in contextual advertising. One is "sensitive content detection" where the advertiser wants to avoid content relating to war, violence, pornography, etc. even if t...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Yi Zhang, Arun C. Surendran, John C. Platt, Mukund Narasimhan
• Keywords: Data Mining, KDD 2008, Page Level Labels, Relevant Content Exists, Sensitive Content Detection