Distinguished Lecture: Audience Selection for On-line Brand Advertising: Privacy-friendly Social Network Targeting

October 29, 2009
2:50 pm - 4:00 pm
Halligan 111A
Speaker: Foster Provost, NYU Stern School of Business
Host: Carla Brodley


I will discuss privacy-friendly methods for finding good audiences for on-line brand advertising, by extracting quasi-social networks from browser behavior on user-generated content sites. Targeting social-network neighbors resonates well with advertisers, and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously. Besides being one of the first studies to our knowledge on data mining for on-line brand advertising, this work makes several important contributions. We introduce a framework for evaluating brand audiences, in analogy to predictive-modeling holdout evaluation. We introduce methods for extracting quasi-social networks from data on visitations to social networking pages. The data are completely anonymous with respect to both browser identity and content. We introduce measures of brand proximity based on measures of graph proximity. We show that audiences with high brand proximity indeed show substantially higher brand affinity. Finally, we provide suggestive evidence that the quasi-social network actually embeds a true social network, which along with results from social theory and prior results on network-based direct marketing, offers an explanation for the increase in brand affinity of the selected audiences.

This study was done in collaboration with Brian Dalessandro, Rod Hook, Xiaohan Zhang, and Alan Murray.

Foster Provost is Professor, NEC Faculty Fellow, and Paduano Fellow of Business Ethics in the Stern School of Business at New York University. He is Chief Scientist for Coriolis Ventures, a NYC-based early stage venture and incubation fund. He is Editor-in-Chief of the journal Machine Learning and was a founding board member of the International Machine Learning Society. Professor Provost's recent research focuses on data mining and machine learning (DM&ML) when data can be acquired selectively at a cost, and on DM&ML with data about social networks. He has published lots of papers about these and other DM&ML topics, has won awards for his research, and has applied the ideas in practice to applications including fraud detection, network diagnosis, targeted marketing, on-line advertising, and others.