Predicting product adoption in large-scale social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Display advertising impact: search lift and social influence
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale behavioral targeting with a social twist
Proceedings of the 20th ACM international conference on Information and knowledge management
Identifying influential agents for advertising in multi-agent markets
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A diffusion mechanism for social advertising over microblogs
Decision Support Systems
Hierarchical influence maximization for advertising in multi-agent markets
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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.