WWW2009 EPrints

Privacy Preserving Frequency Capping in Internet Banner Advertising

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We describe an optimize-and-dispatch approach for delivering pay-per-impression advertisements in online advertising. The platform provider for an advertising network commits to showing advertisers’ banner ads while capping the number of advertising message shown to a unique user as the user transitions through the network. The traditional approach for enforcing frequency caps has been to use crosssite cookies to track users. However,cross-site cookies and other tracking mechanisms can infringe on the user privacy. In this paper, we propose a novel linear programming approach that decides when to show an ad to the user based solely on the page currently viewed by the users. We show that the frequency caps are fulfilled in expectation. We show the efficacy of that approach using simulation results. Categories and Subject Descriptors: G.3 Mathematics of Computing: Probability and Statistics General Terms: Algorithms. Keywords: User Model, Markov Chain to transition from one section of the advertising network to another based on a random yet know probability transition matrix. The traditional approach to frequency capping is to use cross-site cookies to track users through the web properties where the advertising network is serving ads. The cookies are used to keep a count of the number of ads the user has seen. When the user has reached the maximum daily caps, no further ads are shown. However, there has been growing concern over the privacy issues associated with tracking the user across multiple sites. Furthermore,up to 33 % of the users delete their cookies making cookie based approach unreliable [3]. We propose a novel algorithm that can be used to insure that the frequency caps are fulfilled in expectation. The approach is based on formulating a linear optimization program that maximizes the expected number of ads seen by the user subject to the frequency caps constraints. The solution to the linear program gives a set of probabilistic weights used by the ad server to decide whether to serve the ad when a user arrives at a specific web page.

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