Leveraging Wikipedia concept and category information to enhance contextual advertising

Authors:
Zongda Wu;Guandong Xu;Rong Pan;Yanchun Zhang;Zhiwen Hu;Jianfeng Lu
Affiliations:
Wenzhou University, Wenzhou, China;Victoria University, Victoria, Australia;Aalborg University, Aalborg , Denmark;Victoria University, Victoria , Australia;Wenzhou University, Wenzhou, China;Zhejiang Normal University, Jinhua, China
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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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.