An analysis of alternative slot auction designs for sponsored search
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Revenue analysis of a family of ranking rules for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
Greedy bidding strategies for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
An incentive-compatible multi-armed bandit mechanism
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Sponsored search with contexts
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Dynamic cost-per-action mechanisms and applications to online advertising
Proceedings of the 17th international conference on World Wide Web
A Cascade Model for Externalities in Sponsored Search
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Value of learning in sponsored search auctions
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Machine learning in an auction environment
Proceedings of the 23rd international conference on World wide web
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We introduce an exploration scheme aimed at learning advertiser click-through rates in sponsored search auctions with minimal effect on advertiser incentives. The scheme preserves both the current ranking and pricing policies of the search engine and only introduces one parameter which controls the rate of exploration. This parameter can be set so as to allow enough exploration to learn advertiser click-through rates over time, but also eliminate incentives for advertisers to alter their currently submitted bids. When advertisers have much more information than the search engine, we show that although this goal is not achievable, incentives to deviate can be made arbitrarily small by appropriately setting the exploration rate. Given that advertisers do not alter their bids, we bound revenue loss due to exploration.