Semantic Associations For Contextual Advertising


Massimiliano Ciaramita
Vanessa Murdock
Vassilis Plachouras


Contextual advertising systems place ads automatically in Web pages, based on the Web page content. In this paper we present a machine learning approach to contextual advertising using a novel set of features which aims to capture subtle semantic associations between the vocabularies of the ad and the Web page. We design a model for ranking ads with respect to a page which is learned using Support Vector Machines. We evaluate our model on a large set of manually evaluated ad placements. The proposed model significantly improves accuracy over a learned model using features from current work in contextual advertising.

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February, 2008

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