Efficient retrieval for contextual advertising utilizing past click logs

Abstract

Contextual advertising is a form of textual advertising usually displayed on third party Web pages. One of the main problems with contextual advertising is determining how to select ads that are relevant to the page content and/or the user information in order to achieve both effective advertising and a positive user experience. In this study, we propose a translation method that learns the mapping of the contextual information to the textual features of ads by using past click data. The contextual information includes the user’s demographic information and behavioral information as well as page content information. The proposed method is able to retrieve more preferable ads while maintaining the sparsity of the inverted index and the performance of the ad retrieval system. In addition, it is easy to implement and there is no need to modify an existing ad retrieval system. Extensive evaluations showed the effectiveness of our approach.