Audience selection for on-line brand advertising: privacy-friendly social network targeting

This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding good audiences for brand advertising (as opposed to click maximizing, for example). 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 papers to our knowledge on data mining for on-line brand advertising, this paper 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, without collecting any information on the identities of the browsers or the content of the social-network pages. We introduce measures of brand proximity in the network, and show th...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Foster J. Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, Alan Murray
• Keywords: Audience Brand Affinity, Brand Audiences, Data Mining, KDD 2009, On-line Brand Advertising