Leveraging Wikipedia concept and category information to enhance contextual advertising
| Autor: | Zhiwen Hu, Guandong Xu, Yanchun Zhang, Jianfeng Lu, Zongda Wu, Rong Pan |
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| Přispěvatelé: | Berendt, Bettina, Vries, Arjen de, Fan, Wenfei, Macdonald, Craig, Ounis, Iadh, Ruthven, Ian |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: | |
| Zdroj: | Wu, Z, Xu, G, Pan, R, Zhang, Y, Hu, Z & Lu, J 2011, Leveraging Wikipedia concept and category information to enhance contextual advertising . in B Berendt, A D Vries, W Fan, C Macdonald, I Ounis & I Ruthven (eds), Proceedings of the 20th ACM international conference on Information and knowledge management . Association for Computing Machinery, New York, NY, USA, CIKM '11, pp. 2105-2108, 20th ACM Conference on Information and Knowledge Management, Glasgow, United Kingdom, 24/10/2011 . < http://doi.acm.org/10.1145/2063576.2063901 > CIKM |
| DOI: | 10.1145/2063576.2063901 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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