Combining Behavioral and Social Network Data for Online Advertising

There are two main requirements for effective advertising in social networks. The first is that links in the social network are relevant to the targeted ads. The second is that social information can be easily incorporated with existing targeting methods to predict response rates. Our purpose in this paper is to investigate these requirements. We measure the relevance of a social network, the Yahoo! Instant Messenger graph, to classes of ads. We investigate the degree to which social network information complements existing user-profile information for targeting. We find that there is significant evidence in our social network of homophily, that links in the network indicate similar ad-relevant interests. We propose an ensemble classifier to combine existing user-only models with social network features to improve response predictions.