Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
The Journal of Machine Learning Research
An incentive-compatible multi-armed bandit mechanism
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Dynamic cost-per-action mechanisms and applications to online advertising
Proceedings of the 17th international conference on World Wide Web
Game Theoretic Problems in Network Economics and Mechanism Design Solutions
Game Theoretic Problems in Network Economics and Mechanism Design Solutions
A Cascade Model for Externalities in Sponsored Search
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Sponsored Search Auctions with Markovian Users
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
The price of truthfulness for pay-per-click auctions
Proceedings of the 10th ACM conference on Electronic commerce
An adaptive sponsored search mechanism δ-gain truthful in valuation, time, and budget
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Learning and incentives in user-generated content: multi-armed bandits with endogenous arms
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
Which mechanism for sponsored search auctions with externalities?
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Computationally efficient techniques for economic mechanisms
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Sponsored search auctions constitute one of the most successful applications of microeconomic mechanisms. In mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and, at the same time, to assure both the advertisers and the auctioneer a non--negative utility. Nonetheless, in sponsored search auctions, the click-through-rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard incentive compatible mechanisms cannot be directly applied and must be paired with an effective learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a learning mechanism able to estimate the CTRs as the same time as implementing a truthful mechanism with a revenue loss as small as possible compared to an optimal mechanism designed with the true CTRs. Previous works showed that in single-slot auctions the problem can be solved using a suitable exploration-exploitation mechanism able to achieve a per-step regret of order O(T-1/3) (where T is the number of times the auction is repeated). In this paper we extend these results to the general case of contextual multi-slot auctions with position- and ad-dependent externalities. In particular, we prove novel upper-bounds on the revenue loss w.r.t. to a VCG auction and we report numerical simulations investigating their accuracy in predicting the dependency of the regret on the number of rounds T, the number of slots K, and the number of advertisements n.