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.
