Translation method of contextual information into textual space of advertisements

Yukihiro Tagami, Toru Hotta, Yusuke Tanaka, Shingo Ono, Koji Tsukamoto, Akira Tajima

Abstract: 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.