Difference between revisions of "Leveraging Wikipedia Concept and Category Information to Enhance Contextual Advertising"

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{{Infobox work
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| title = Leveraging Wikipedia Concept and Category Information to Enhance Contextual Advertising
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| date = 2011
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| authors = [[Zongda Wu]]<br />[[Guandong Xu]]<br />[[Rong Pan]]<br />[[Yanchun Zhang]]<br />[[Zhiwen Hu]]<br />[[Jianfeng Lu]]
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| doi = 10.1145/2063576.2063901
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| link = http://dl.acm.org/citation.cfm?id=2063576.2063901
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| plink = https://www.researchgate.net/profile/Guandong_Xu/publication/221614299_Leveraging_Wikipedia_concept_and_category_information_to_enhance_contextual_advertising/links/09e4151286b044fbf0000000.pdf
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}}
 
'''Leveraging Wikipedia Concept and Category Information to Enhance Contextual Advertising''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Zongda Wu]], [[Guandong Xu]], [[Rong Pan]], [[Yanchun Zhang]], [[Zhiwen Hu]] and [[Jianfeng Lu]].
 
'''Leveraging Wikipedia Concept and Category Information to Enhance Contextual Advertising''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Zongda Wu]], [[Guandong Xu]], [[Rong Pan]], [[Yanchun Zhang]], [[Zhiwen Hu]] and [[Jianfeng Lu]].
  
 
== Overview ==
 
== Overview ==
 
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, authors 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, authors map each ad and page into a keyword vector, a concept vector and a category vector. Next, authors select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, authors evaluate approach by using real ads, pages, as well as a great number of concepts and [[categories]] of Wikipedia. Experimental results show that approach can improve the precision of ads-selection effectively.
 
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, authors 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, authors map each ad and page into a keyword vector, a concept vector and a category vector. Next, authors select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, authors evaluate approach by using real ads, pages, as well as a great number of concepts and [[categories]] of Wikipedia. Experimental results show that approach can improve the precision of ads-selection effectively.

Revision as of 10:45, 27 March 2020


Leveraging Wikipedia Concept and Category Information to Enhance Contextual Advertising
Authors
Zongda Wu
Guandong Xu
Rong Pan
Yanchun Zhang
Zhiwen Hu
Jianfeng Lu
Publication date
2011
DOI
10.1145/2063576.2063901
Links
Original Preprint

Leveraging Wikipedia Concept and Category Information to Enhance Contextual Advertising - scientific work related to Wikipedia quality published in 2011, written by Zongda Wu, Guandong Xu, Rong Pan, Yanchun Zhang, Zhiwen Hu and Jianfeng Lu.

Overview

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, authors 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, authors map each ad and page into a keyword vector, a concept vector and a category vector. Next, authors select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, authors evaluate approach by using real ads, pages, as well as a great number of concepts and categories of Wikipedia. Experimental results show that approach can improve the precision of ads-selection effectively.