Abstract
Advertising via the Internet is a significant industry; however, in many ways, the industry is still in its infancy and still requires significant refinement to achieve its full potential. In contextual advertising (CA), the ad-network places ads related to the content of the publishers' webpages. In this article, the authors introduce an approach to implement a CA system for an ad-network. Their contributions are threefold: First, they propose schemes to prepare feature vectors of a webpage for the purpose of classification by its subject. To do so, the authors extract information from its peer webpages as well. Secondly, they prepare a suitable taxonomy from ODP. This taxonomy fulfils the requirements of a CA system such as broad coverage of semantically relevant topics etc. Thirdly, they conduct experiments on the proposed CA system architecture. The results establish the competence of the proposed approach. The authors empirically establish that the scheme which extracts information from the intersection of cues from web accessibility and search engine optimisation, of the target webpage provides the best accuracy among all the CA systems.
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