Primal-dual aggregation and disaggregation for stochastic linear programs
Mathematics of Operations Research
A posteriori error bounds in linear programming aggregation
Computers and Operations Research
Unintrusive customization techniques for Web advertising
WWW '99 Proceedings of the eighth international conference on World Wide Web
An entropy approach to unintrusive targeted advertising on the Web
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Traffic model and performance evaluation of Web servers
Performance Evaluation
Broadcast Scheduling for Mobile Advertising
Operations Research
Performance bounds of algorithms for scheduling advertisements on a web page
Journal of Scheduling
Scheduling Banner Advertisements on the Web
INFORMS Journal on Computing
Improvements to the Linear Programming Based Scheduling of Web Advertisements
Electronic Commerce Research
Nonstationary Poisson modeling of web browsing session arrivals
Information Processing Letters
Estimating rates of rare events at multiple resolutions
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
AdWords and generalized online matching
Journal of the ACM (JACM)
Bidding for Representative Allocations for Display Advertising
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
OR PRACTICE---Scheduling of Dynamic In-Game Advertising
Operations Research
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As targeted advertising becomes prevalent in a wide variety of media vehicles, planning models become increasingly important to ad networks that need to match ads to appropriate audience segments, provide a high quality of service (meet advertisers' goals), and ensure that ad serving opportunities are not wasted. We define Guaranteed Targeted Display Advertising (GTDA) as a class of media vehicles that include webpage banner ads, video games, electronic outdoor billboards, and the next generation of digital TV, and formulate the GTDA planning problem as a transportation problem with quadratic objective. By modeling audience uncertainty, forecast errors, and the ad server's execution of the plan, we derive sufficient conditions that state when our quadratic objective is a good surrogate for several ad delivery performance metrics. Moreover, our quadratic objective allows us to construct duality-based bounds for evaluating aggregations of the audience space, leading to two efficient algorithms for solving large problems: the first intelligently refines the audience space into successively smaller blocks, and the second uses scaling to find a feasible solution given a fixed audience space partition. Near-optimal schedules can often be produced despite significant aggregation.