AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
The adwords problem: online keyword matching with budgeted bidders under random permutations
Proceedings of the 10th ACM conference on Electronic commerce
Online Ad Assignment with Free Disposal
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Online primal-dual algorithms for maximizing ad-auctions revenue
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Optimal online assignment with forecasts
Proceedings of the 11th ACM conference on Electronic commerce
Balanced allocation with succinct representation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Real-time bidding algorithms for performance-based display ad allocation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Simultaneous approximations for adversarial and stochastic online budgeted allocation
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Real time bid optimization with smooth budget delivery in online advertising
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
A distributed algorithm for large-scale generalized matching
Proceedings of the VLDB Endowment
Partner tiering in display advertising
Proceedings of the 7th ACM international conference on Web search and data mining
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Display ads on the Internet are often sold in bundles of thousands or millions of impressions over a particular time period, typically weeks or months. Ad serving systems that assign ads to pages on behalf of publishers must satisfy these contracts, but at the same time try to maximize overall quality of placement. This is usually modeled in the literature as an online allocation problem, where contracts are represented by overall delivery constraints over a finite time horizon. However this model misses an important aspect of ad delivery: time homogeneity. Advertisers who buy these packages expect their ad to be shown smoothly throughout the purchased time period, in order to reach a wider audience, to have a sustained impact, and to support the ads they are running on other media (e.g., television). In this paper we formalize this problem using several nested packing constraints, and develop a tight (1-1/e)-competitive online algorithm for this problem. Our algorithms and analysis require novel techniques as they involve online computation of multiple dual variables per ad. We then show the effectiveness of our algorithms through exhaustive simulation studies on real data sets.