(An) effective contextual advertising using wikipedia matching

Abstract

Contextual advertising is an important part of today`s Web. It provides benefits to all parties: Web site owners and an advertising platform share the revenue, advertisers receive new customers, and Web site visitors get useful reference links. The relevance of selected ads for a Web page is essential for the whole system to work. Problems such as homonymy and polysemy, lack of keywords and context mismatch can lead to the selection of irrelevant ads. Therefore, a simple keyword matching technique gives a poor accuracy. In this thesis, we propose a method for improving the relevance of contextual ads. We propose a novel ``Wikipedia matching`` technique that uses Wikipedia articles as ``reference points`` for ads selection. We show how to combine our new method with existing solutions in order to increase the overall performance. An experimental evaluation based on a set of real ads and a set of pages from news Web sites is conducted. Test results show that our proposed method performs better than existing matching strategies and using the Wikipedia matching in combination with existing approaches provides up to 50\% lift in the average precision. TREC standard measure bpref-10 also confirms the positive effect of using Wikipedia matching for the effective ads selection.