Leveraging Wikipedia concept and category information to enhance contextual advertising

Wu, Z; Xu, G; Pan, R; Zhang, Y; Hu, Z; Lu, J

Leveraging Wikipedia concept and category information to enhance contextual advertising

Wu, Z Xu, G

Pan, R Zhang, Y Hu, Z Lu, J

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Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 2105 - 2108
Issue Date:: 2011-12-13

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Field	Value	Language
dc.contributor.author	Wu, Z	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Pan, R	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Hu, Z	en_US
dc.contributor.author	Lu, J	en_US
dc.date.issued	2011-12-13	en_US
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2011, pp. 2105 - 2108	en_US
dc.identifier.isbn	9781450307178	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31077
dc.description.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. © 2011 ACM.	en_US
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings	en_US
dc.relation.isbasedon	10.1145/2063576.2063901	en_US
dc.title	Leveraging Wikipedia concept and category information to enhance contextual advertising	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Glasgow, United Kingdom
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

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. © 2011 ACM.

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http://hdl.handle.net/10453/31077