Article

Ad Exchanges: Research Issues

Author:

S. MuthukrishnanAuthors Info & Claims

WINE '09: Proceedings of the 5th International Workshop on Internet and Network Economics

Pages 1 - 12

https://doi.org/10.1007/978-3-642-10841-9_1

Published: 09 December 2009 Publication History

Abstract

An emerging way to sell and buy display ads on the Internet is via ad exchanges. RightMedia [1], AdECN [2] and DoubleClick Ad Exchange [3] are examples of such real-time two-sided markets. We describe an abstraction of this market. Based on that abstraction, we present several research directions and discuss some insights.

References

[1]

RightMedia, http://www.rightmedia.com/ More info at, http://www.rightmedia.com/right-media-101/

[2]

AdECN. http://www.adecn.com/. Whitepaper at, http://www.adecn.com/resources/ A True Exchange for Online Advertising.pdf. Presentation at, http://www.adecn.com/resources/AdECNPresentation.pps

[3]

DoubleClick Ad Exchange, http://www.doubleclick.com/products/advertisingexchange/index.aspx

[4]

O'Hara, M.: Market Microstructure Theory. Blackwell, Oxford (1995)

[5]

Madhavan, A.: Market Microstructure: A Survey. Journal of Financial Markets 3(3), 205-258 (2000)

[6]

Muthukrishnan, S.: Internet Ad Auctions: Insights and Directions. In: Aceto, L., Damgård, I., Goldberg, L.A., Halldórsson, M.M., Ingólfsdóttir, A., Walukiewicz, I. (eds.) ICALP 2008, Part I. LNCS, vol. 5125, pp. 14-23. Springer, Heidelberg (2008)

Digital Library

[7]

Klemperer, P.: Auction Theory: A Guide to the Literature. J. of Economic Surveys, 227-286 (1999)

[8]

Varian, H.: Position Auctions. Intl. J. of Industrial Organization (2006)

[9]

Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. American Economic Review 97(1), 242-259 (2007)

[10]

Vickrey, W.: Counterspeculation, Auctions, and Competitive Sealed Tenders. Journal of Finance 16, 8-37 (1961)

[11]

Myerson, R.: Optimal auction design. Math of Operations Research 6, 58-73 (1981)

Digital Library

[12]

Feldman, J., Mirrokni, V., Muthukrishnan, S., Pai, M.: Charaterizing Equilibria of Auctions with Mediators (manuscript, 2009)

[13]

Ghosh, A., Rubinstein, B., Vassilvitskii, S., Zinkevich, M.: Adaptive bidding for display advertising. In: Proc. WWW (2009)

Digital Library

[14]

Lu, P., Teng, S., Yu, C.: Truthful Auctions with Optimal Profit. In: Spirakis, P.G., Mavronicolas, M., Kontogiannis, S.C. (eds.) WINE 2006. LNCS, vol. 4286, pp. 27-36. Springer, Heidelberg (2006)

Digital Library

[15]

Baliga, S., Vohra, R.: Market Research and Market Design (2003), http://www.kellogg.northwestern.edu/faculty/baliga/htm/mrandmd.pdf

[16]

Aggarwal, G., Goel, G., Mehta, A.: Efficiency of (revenue-)optimal mechanisms. In: ACM Conference on Electronic Commerce (EC), pp. 235-242 (2009)

Digital Library

[17]

Mirrokni, V., Muthukrishnan, S., Nadav, U.: Quasi-Proportional Mechanisms: Prior-free Revenue Maximization. arXivs (2009), http://arxiv1.library.cornell.edu/pdf/0909.5365v1

[18]

Even Dar, E., Mansour, Y., Nadav, U.: Convergence in Proportional Games. In: Proc. STOC (2009)

[19]

Chakraborty, T., Even-Dar, E., Guha, S., Mansour, Y., Muthukrishnan, S.: Callout optimization (manuscript, 2009)

[20]

Parkes, D., Rabin, M., Shieber, S., Thorpe, C.: Practical secrecy-preserving, verifiably correct and trustworthy auctions. In: Proc. EC, pp. 70-81 (2006)

Digital Library

[21]

Ghosh, A., McAfee, P., Papineni, K., Vassilvitskii, S.: Bidding for Representative Allocations for Display Advertising. In: Leonardi, S. (ed.) WINE 2009. LNCS, vol. 5929, pp. 208-219. Springer, Heidelberg (2009)

Digital Library

[22]

Rabin, M., Muthukrishnan, S., Yung, M.: Fast, communication efficient, provably secure protocols for real-time Vickrey auctions (manuscript, 2009)

[23]

Muthukrishnan, S.: Bidding on Configurations in Internet Ad Auctions. In: Ngo, H.Q. (ed.) COCOON 2009. LNCS, vol. 5609, pp. 1-6. Springer, Heidelberg (2009)

Digital Library

[24]

Lahaie, S., Parkes, D., Pennock, D.: An Expressive Auction Design for Online Display Advertising. In: AAAI, pp. 108-113 (2008)

Digital Library

Cited By

Bhawalkar KGuruganesh GLahaie SPerlroth ASivan B(2022)Research Challenges in Internet Ad Markets: Vignettes on Complex EnvironmentsACM SIGecom Exchanges10.1145/3699804.369980820:2(48-61)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1145/3699804.3699808
Halbersberg DHalevi MSalhov M(2022)Search and Score-Based Waterfall Auction OptimizationLearning and Intelligent Optimization10.1007/978-3-031-24866-5_27(361-378)Online publication date: 5-Jun-2022
https://dl.acm.org/doi/10.1007/978-3-031-24866-5_27
Yang HWang TTang XLi QShi YJiang SYu HSong HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Multi-task Learning for Bias-Free Joint CTR Prediction and Market Price Modeling in Online AdvertisingProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482373(2291-2300)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482373
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Published In

cover image Guide Proceedings

WINE '09: Proceedings of the 5th International Workshop on Internet and Network Economics

December 2009

639 pages

ISBN:9783642108402

Editor:
Stefano Leonardi
Department of Information and Systems, Sapienza University of Rome, Roma, Italy 00185

Copyright © Springer-Verlag Berlin Heidelberg.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 09 December 2009

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Bhawalkar KGuruganesh GLahaie SPerlroth ASivan B(2022)Research Challenges in Internet Ad Markets: Vignettes on Complex EnvironmentsACM SIGecom Exchanges10.1145/3699804.369980820:2(48-61)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1145/3699804.3699808
Halbersberg DHalevi MSalhov M(2022)Search and Score-Based Waterfall Auction OptimizationLearning and Intelligent Optimization10.1007/978-3-031-24866-5_27(361-378)Online publication date: 5-Jun-2022
https://dl.acm.org/doi/10.1007/978-3-031-24866-5_27
Yang HWang TTang XLi QShi YJiang SYu HSong HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Multi-task Learning for Bias-Free Joint CTR Prediction and Market Price Modeling in Online AdvertisingProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482373(2291-2300)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482373
Lejeune MTurner J(2019)Planning Online Advertising Using Gini IndicesOperations Research10.1287/opre.2019.184167:5(1222-1245)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1287/opre.2019.1841
Balseiro SBesbes OWeintraub G(2019)Dynamic Mechanism Design with Budget-Constrained Buyers Under Limited CommitmentOperations Research10.1287/opre.2018.183067:3(711-730)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1287/opre.2018.1830
Avni GHenzinger TChonev V(2019)Infinite-duration Bidding GamesJournal of the ACM10.1145/334029566:4(1-29)Online publication date: 16-Jul-2019
https://dl.acm.org/doi/10.1145/3340295
Kanoria YNazerzadeh H(2019)Incentive-Compatible Learning of Reserve Prices for Repeated AuctionsCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316526(932-933)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316526
Ren KQin JZheng LYang ZZhang WYu YTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Deep Landscape Forecasting for Real-time Bidding AdvertisingProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330870(363-372)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330870
Refaei Afshar RZhang YFirat MKaymak U(2019)Reinforcement Learning Method for Ad Networks Ordering in Real-Time BiddingAgents and Artificial Intelligence10.1007/978-3-030-37494-5_2(16-36)Online publication date: 19-Feb-2019
https://dl.acm.org/doi/10.1007/978-3-030-37494-5_2
Paliwal PRenov O(2019)Gradient Boosting Censored Regression for Winning Price Prediction in Real-Time BiddingDatabase Systems for Advanced Applications10.1007/978-3-030-18590-9_43(348-352)Online publication date: 22-Apr-2019
https://dl.acm.org/doi/10.1007/978-3-030-18590-9_43
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