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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 counter intuitively can allow the identification of good audiences anonymously. Besides being one of the first papers to the knowledge on data mining for on-line brand advertising, this paper makes several important contributions.
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