Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
The multiple attribution problem in pay-per-conversion advertising
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Understanding Crowds' Migration on the Web
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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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 cross-site 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.