Toru Hotta

Translation method of contextual information into textual space of advertisements

Contextual advertising has a key problem to determine how to select the ads that are relevant to the page content and/or the user information. We introduce a translation method that learns a mapping of contextual information to the textual features of ads by using past click data. This method is easy to implement and there is no need to modify an ordinary ad retrieval system because the contextual feature vector is simply transformed into a term vector with the learned matrix. We applied our approach with a real ad serving system and compared the online performance in A/B testing.

Filling context-ad vocabulary gaps with click logs

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. Typically, the relevance of an ad to page content is indicated by a tf-idf score that measures the word overlap between the page and the ad content, so this problem is transformed into a similarity search in a vector space. However, such an approach is not useful if the vocabulary used on the page is expected to be different from that in the ad. There have been studies proposing the use of semantic categories or hidden classes to overcome this problem. With these approaches it is necessary to expand the ad retrieval system or build new index to handle the categories or classes, and it is not always easy to maintain the number of categories and classes required for business needs. In this work, 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 users 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. We evaluated this approach offline on a data set based on logs from an ad network. Our method achieved better results than existing methods. We also applied our approach with a real ad serving system and compared the online performance using A/B testing. Our approach achieved an improvement over the existing production system.