Abstract
As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant ads into a Web page, so as to increase the number of ad-clicks. However, some problems of homonymy and polysemy, low intersection of keywords etc., can lead to the selection of irrelevant ads for a page. In this paper, we present a new contextual advertising approach to overcome the problems, which uses Wikipedia concept and category information to enrich the content representation of an ad (or a page). First, we map each ad and page into a keyword vector, a concept vector and a category vector. Next, we select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, we evaluate our approach by using real ads, pages, as well as a great number of concepts and categories of Wikipedia. Experimental results show that our approach can improve the precision of ads-selection effectively. Copyright © 2011 ACM.
| Original language | English |
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| Title of host publication | Proceedings of the 20th ACM international conference on Information and knowledge management |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery |
| Pages | 2105-2108 |
| ISBN (Print) | 9781450307178 |
| DOIs | |
| Publication status | Published - 2011 |