Improving contextual advertising matching by using Wikipedia thesaurus knowledge

Xu, G; Wu, Z; Li, G; Chen, E

Improving contextual advertising matching by using Wikipedia thesaurus knowledge

Xu, G

Wu, Z Li, G Chen, E

Permalink

Publication Type:: Journal Article
Citation:: Knowledge and Information Systems, 2015, 43 (3), pp. 599 - 631
Issue Date:: 2015-06-01

Closed Access

	Filename	Description	Size
	KAIS.pdf	Published Version	1.64 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Wu, Z	en_US
dc.contributor.author	Li, G	en_US
dc.contributor.author	Chen, E	en_US
dc.date.issued	2015-06-01	en_US
dc.identifier.citation	Knowledge and Information Systems, 2015, 43 (3), pp. 599 - 631	en_US
dc.identifier.issn	0219-1377	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33769
dc.identifier.uri	http://hdl.handle.net/10453/36551
dc.identifier.uri	http://hdl.handle.net/10453/36552
dc.description.abstract	© 2014, Springer-Verlag London. As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant commercial ads within the content of a Web page, to provide a better user experience and as a result increase the user’s ad-click rate. However, due to the intrinsic problems of homonymy and polysemy, the low intersection of keywords, and a lack of sufficient semantics, traditional keyword matching techniques are not able to effectively handle contextual matching and retrieve relevant ads for the user, resulting in an unsatisfactory performance in ad selection. In this paper, we introduce a new contextual advertising approach to overcome these problems, which uses Wikipedia thesaurus knowledge to enrich the semantic expression of a target page (or an ad). First, we map each page into a keyword vector, upon which two additional feature vectors, the Wikipedia concept and category vector derived from the Wikipedia thesaurus structure, are then constructed. Second, to determine the relevant ads for a given page, we propose a linear similarity fusion mechanism, which combines the above three feature vectors in a unified manner. Last, we validate our approach using a set of real ads, real pages along with the external Wikipedia thesaurus. The experimental results show that our approach outperforms the conventional contextual advertising matching approaches and can substantially improve the performance of ad selection.	en_US
dc.relation.ispartof	Knowledge and Information Systems	en_US
dc.relation.isbasedon	10.1007/s10115-014-0745-z	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Improving contextual advertising matching by using Wikipedia thesaurus knowledge	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	43	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
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.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	43	en_US

Abstract:

© 2014, Springer-Verlag London. As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant commercial ads within the content of a Web page, to provide a better user experience and as a result increase the user’s ad-click rate. However, due to the intrinsic problems of homonymy and polysemy, the low intersection of keywords, and a lack of sufficient semantics, traditional keyword matching techniques are not able to effectively handle contextual matching and retrieve relevant ads for the user, resulting in an unsatisfactory performance in ad selection. In this paper, we introduce a new contextual advertising approach to overcome these problems, which uses Wikipedia thesaurus knowledge to enrich the semantic expression of a target page (or an ad). First, we map each page into a keyword vector, upon which two additional feature vectors, the Wikipedia concept and category vector derived from the Wikipedia thesaurus structure, are then constructed. Second, to determine the relevant ads for a given page, we propose a linear similarity fusion mechanism, which combines the above three feature vectors in a unified manner. Last, we validate our approach using a set of real ads, real pages along with the external Wikipedia thesaurus. The experimental results show that our approach outperforms the conventional contextual advertising matching approaches and can substantially improve the performance of ad selection.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/33769