ABSTRACT
Ad exchange platforms connect online publishers and advertisers and facilitate the sale of billions of impressions every day. We study these environments from the perspective of a publisher who wants to find the profit-maximizing exchange in which to sell his inventory. Ideally, the publisher would run an auction among exchanges. However, this is not usually possible due to practical business considerations. Instead, the publisher must send each impression to only one of the exchanges, along with an asking price. We model the problem as a variation of the multi-armed bandits problem in which exchanges (arms) can behave strategically in order to maximizes their own profit. We propose e mechanisms that find the best exchange with sub-linear regret and have desirable incentive properties.
- Shipra Agrawal and Nikhil R. Devanur. 2014. Bandits with Concave Rewards and Convex Knapsacks. In Proceedings of the Fifteenth ACM Conference on Economics and Computation. 989--1006. Google ScholarDigital Library
- Kareem Amin, Afshin Rostamizadeh, and Umar Syed. 2013. Learning Prices for Repeated Auctions with Strategic Buyers. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS). 1169--1177.Google Scholar
- Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning 47, 2--3 (2002), 235--256. Google ScholarDigital Library
- Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, and Aleksandrs Slivkins. 2012. Dynamic Pricing with Limited Supply. In Proceedings of the 13th ACM Conference on Electronic Commerce (EC '12). 74--91. ISBNx978-1-4503-1415-2 Google ScholarDigital Library
- Moshe Babaioff, Robert Kleinberg, and Aleksandrs Slivkins. 2010. Truthful Mechanisms with Implicit Payment Computation. In ACM Conference on Electronic Commerce. Google ScholarDigital Library
- Moshe Babaioff, Yogeshwer Sharma, and Aleksandrs Slivkins. 2009. Characterizing truthful multi-armed bandit mechanisms. In ACM Conference on Electronic Commerce. 79--88. Google ScholarDigital Library
- Ashwinkumar Badanidiyuru, Robert Kleinberg, and Aleksandrs Slivkins. 2013. Bandits with knapsacks. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. IEEE, 207--216. Google ScholarDigital Library
- Dirk Bergemann and Juuso Välimäki. 2010. The Dynamic Pivot Mechanism. Econometrica 78 (2010), 771--789.Google ScholarCross Ref
- Omar Besbes and Assaf Zeevi. 2009. Dynamic pricing without knowing the demand function: risk bounds and near-optimal algorithms. Operations Research 57 (2009), 1407--1420. Google ScholarDigital Library
- Sébastien Bubeck and Aleksandrs Slivkins. The Best of Both Worlds: Stochastic and Adversarial Bandits. In The 25th Annual Conference on Learning Theory (COLT), June 25-27, 2012, Edinburgh, Scotland, pages = 1--23, year = 2012.Google Scholar
- L. Elisa Celis, Gregory Lewis, Markus Mobius, and Hamid Nazerzadeh. 2014. Buy-it-Now or Take-a-Chance: Price Discrimination through Randomized Auctions. Management Science (2014).Google Scholar
- Nicolò Cesa-Bianchi, Claudio Gentile, and Yishay Mansour. 2013. Regret Minimization for Reserve Prices in Second-Price Auctions. In Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). 1190--1204. Google ScholarDigital Library
- Nikhil R. Devanur and Sham M. Kakade. 2009. The price of truthfulness for pay-per-click auctions. In ACM Conference on Electronic Commerce. 99--106. Google ScholarDigital Library
- Jason Hartline, Vasilis Syrgkanis, and Eva Tardos. 2015. No-Regret Learning in Bayesian Games. In Advances in Neural Information Processing Systems. 3043--3051. Google ScholarDigital Library
- Sham M. Kakade, Ilan Lobel, and Hamid Nazerzadeh. 2013. Optimal Dynamic Mechanism Design and the Virtual Pivot Mechanism. Operations Research 61, 4 (2013), 837--854.Google ScholarCross Ref
- Yash Kanoria and Hamid Nazerzadeh. 2014. Dynamic Reserve Prices for Repeated Auctions: Learning from Bids - Working Paper. In Web and Internet Economics - 10th International Conference (WINE). 232.Google Scholar
- Robert Kleinberg and Tom Leighton. 2003. The value of knowing a demand curve: Bounds on regret for online posted-price auctions. In Proceedings of 44th Annual IEEE Symposium on Foundations of Computer Science. 594--605. Google ScholarDigital Library
- Matoušek and Jan Vondrák. 2001. The probabilistic method. Lecture Notes, Department of Applied Mathematics, Charles University, Prague (2001).Google Scholar
- R Preston McAfee and Sergei Vassilvitskii. 2012. An overview of practical exchange design. Current Science(Bangalore) 103, 9 (2012), 1056--1063.Google Scholar
- Mehryar Mohri and Andres Muñoz Medina. 2014a. Learning Theory and Algorithms for revenue optimization in second price auctions with reserve. In Proceedings of the 31th International Conference on Machine Learning (ICML). 262--270.Google Scholar
- Mehryar Mohri and Andres Muñoz Medina. 2014b. Revenue Optimization in Posted-Price Auctions with Strategic Buyers. NIPS (2014). Google ScholarDigital Library
- Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2012. Foundations of machine learning. MIT press. Google ScholarDigital Library
- S. Muthukrishnan. 2009. Ad Exchanges: Research Issues. In Internet and Network Economics, 5th International Workshop (WINE). 1--12. Google ScholarDigital Library
- Hamid Nazerzadeh, Amin Saberi, and Rakesh Vohra. 2013. Dynamic Cost-Per-Action Mechanisms and Applications to Online Advertising. Operations Research 61, 1 (2013), 98--111. Google ScholarDigital Library
- Denis Nekipelov, Vasilis Syrgkanis, and Eva Tardos. 2015. Econometrics for learning agents. In Proceedings of the Sixteenth ACM Conference on Economics and Computation. ACM, 1--18. Google ScholarDigital Library
- Zizhuo Wang, Shiming Deng, and Yinyu Ye. 2014. Close the Gaps: A Learning-While-Doing Algorithm for Single-Product Revenue Management Problems. Operations Research 62, 2 (2014), 318--331. Google ScholarDigital Library
Index Terms
Where to Sell: Simulating Auctions From Learning Algorithms
Recommendations
How to Sell a Dataset? Pricing Policies for Data Monetization
EC '19: Proceedings of the 2019 ACM Conference on Economics and ComputationThe wide variety of pricing policies used in practice by data-sellers suggests that there are significant challenges in pricing datasets. The selling of a dataset -- arranged in a row-column format, where rows represent records and columns represent ...
Pricing commodities, or how to sell when buyers have restricted valuations
WAOA'07: Proceedings of the 5th international conference on Approximation and online algorithmsHow should a seller price his goods in a market where each buyer prefers a single good among his desired goods, and will buy the cheapest such good, as long as it is within his budget? We provide efficient algorithms that compute near-optimal prices for ...
Comments