Leveraging Wikipedia concept and category information to enhance contextual advertising

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Wu, Zongda, Xu, Guandong, Pan, Rong, Zhang, Yanchun, Hu, Zhiwen and Lu, Jianfeng (2011) Leveraging Wikipedia concept and category information to enhance contextual advertising. In: CIKM '11 : proceedings of the 20th ACM international conference on Information and knowledge management. Berendt, Bettina, de Vries, Arjen, Fan, Wenfei, Macdonald, Craig, Ounis, Iadh and Ruthven, Ian, eds. Association for Computing Machinery, New York, pp. 2105-2108.


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. ---Conference held 24-28 October, 2011, Glasgow, Scotland

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Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/9814
DOI 10.1145/2063576.2063901
Official URL http://dl.acm.org/citation.cfm?id=2063901&preflayo...
ISBN 9781450307178
Subjects Historical > FOR Classification > 0804 Data Format
Historical > SEO Classification > 8902 Computer Software and Services
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID22797, concepts, concept vector, category vector construction, similarity computation, running performance, algorithms
Citations in Scopus 7 - View on Scopus
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