Competitive auctions and digital goods
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Truthful approximation mechanisms for restricted combinatorial auctions: extended abstract
Eighteenth national conference on Artificial intelligence
Optimization in the private value model: competitive analysis applied to auction design
Optimization in the private value model: competitive analysis applied to auction design
Multi-unit auctions with budget-constrained bidders
Proceedings of the 6th ACM conference on Electronic commerce
Revenue maximization when bidders have budgets
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Truthful auctions for pricing search keywords
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Multi-unit auctions with unknown supply
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
INFORMS Journal on Computing
Revenue analysis of a family of ranking rules for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
Click fraud resistant methods for learning click-through rates
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Stochastic variability in sponsored search auctions: observations and models
Proceedings of the 12th ACM conference on Electronic commerce
Sponsored search auction without bias
Proceedings of the 14th Annual International Conference on Electronic Commerce
Ranking and tradeoffs in sponsored search auctions
Proceedings of the fourteenth ACM conference on Electronic commerce
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We consider the problemof designing auctions with worst case revenue guarantees for sponsored search. In contrast with other settings, ad dependent clickthrough rates lead to two natural posted-price benchmarks. In one benchmark, winning advertisers are charged the same price per click, and in the other, the product of the price per click and the advertiser clickability (which can be thought of as the probability an advertisement is clicked if it has been seen) is the same for all winning advertisers. We adapt the random sampling auction from [9] to the sponsored search setting and improve the analysis from [1], to show a high competitive ratio for two truthful auctions, each with respect to one of the two described benchmarks. However, the two posted price benchmarks (and therefore the revenue guarantees from the corresponding random sampling auctions) can each be larger than the other; further, which is the larger cannot be determined without knowing the private values of the advertisers. We design a new auction, that incorporates these two random sampling auctions, with the following property: the auction has a Nash equilibrium, and every equilibrium has revenue at least the larger of the revenues raised by running each of the two auctions individually (assuming bidders bid truthfully when doing so is a utility maximizing strategy). Finally, we perform simulations which indicate that the revenue from our auction outperforms that from the VCG auction in less competitive markets.