IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v282y2020i2p696-711.html
   My bibliography  Save this article

Learning and pricing models for repeated generalized second-price auction in search advertising

Author

Listed:
  • Yang, Wei
  • Xiao, Baichun
  • Wu, Lifang

Abstract

In search advertising how much an advertiser is willing to pay for a click or tap on his search ad is private information, which hampers an ad seller’s ability to set the best reserve price to increase the revenue for the generalized second-price (GSP) auctions used to allocate ad slots. We present a series of learning and pricing models for repeated GSP auctions selling multiple heterogeneous items. This paper contributes to the literature in dynamic pricing with learning and complements the existing off-line studies on impact of the reserve price in the multi-billion dollar online advertising business. With few restrictions on the distribution function of the unknown parameter, algorithms are developed to estimate the empirical distribution function and determine the best reserve price to reduce the revenue loss (regret) over time. When bidders bid in the locally envy-free equilibrium, we present an algorithm that has the best attainable regret upper bound. When bidders do not bid in the locally envy-free equilibrium, we propose a GSP auction with position-specific reserve prices and develop an algorithm with the same regret bound to mitigate the risk of strategic bidding. With a high volatility involved, learning becomes more active while earning is more effective. When bidders coordinate bidding, the properly selected starting reserve prices can substantially reduce the revenue loss from possible collusive bidding behaviors.

Suggested Citation

  • Yang, Wei & Xiao, Baichun & Wu, Lifang, 2020. "Learning and pricing models for repeated generalized second-price auction in search advertising," European Journal of Operational Research, Elsevier, vol. 282(2), pages 696-711.
  • Handle: RePEc:eee:ejores:v:282:y:2020:i:2:p:696-711
    DOI: 10.1016/j.ejor.2019.09.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221719308082
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2019.09.051?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jennifer Brown & John Morgan, 2009. "How Much Is a Dollar Worth? Tipping versus Equilibrium Coexistence on Competing Online Auction Sites," Journal of Political Economy, University of Chicago Press, vol. 117(4), pages 668-700, August.
    2. Benjamin Edelman & Michael Schwarz, 2010. "Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions," Harvard Business School Working Papers 10-054, Harvard Business School.
    3. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
    4. Bulow, Jeremy & Roberts, John, 1989. "The Simple Economics of Optimal Auctions," Journal of Political Economy, University of Chicago Press, vol. 97(5), pages 1060-1090, October.
    5. J. Michael Harrison & N. Bora Keskin & Assaf Zeevi, 2012. "Bayesian Dynamic Pricing Policies: Learning and Earning Under a Binary Prior Distribution," Management Science, INFORMS, vol. 58(3), pages 570-586, March.
    6. Benjamin Edelman & Michael Schwarz, 2010. "Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions," American Economic Review, American Economic Association, vol. 100(2), pages 597-602, May.
    7. Victor F. Araman & René Caldentey, 2009. "Dynamic Pricing for Nonperishable Products with Demand Learning," Operations Research, INFORMS, vol. 57(5), pages 1169-1188, October.
    8. Zizhuo Wang & Shiming Deng & Yinyu Ye, 2014. "Close the Gaps: A Learning-While-Doing Algorithm for Single-Product Revenue Management Problems," Operations Research, INFORMS, vol. 62(2), pages 318-331, April.
    9. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    10. Omar Besbes & Assaf Zeevi, 2009. "Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms," Operations Research, INFORMS, vol. 57(6), pages 1407-1420, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Symitsi, Efthymia & Markellos, Raphael N. & Mantrala, Murali K., 2022. "Keyword portfolio optimization in paid search advertising," European Journal of Operational Research, Elsevier, vol. 303(2), pages 767-778.
    2. Xiao, Baichun & Yang, Wei, 2021. "A Bayesian learning model for estimating unknown demand parameter in revenue management," European Journal of Operational Research, Elsevier, vol. 293(1), pages 248-262.
    3. Tao Wang, 2023. "A Study on the Choice of Online Marketplace Co-Opetition Strategy Considering the Promotional Behavior of a Store on an E-Commerce Platform," Mathematics, MDPI, vol. 11(10), pages 1-16, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Ostrovsky & Michael Schwarz, 2023. "Reserve Prices in Internet Advertising Auctions: A Field Experiment," Journal of Political Economy, University of Chicago Press, vol. 131(12), pages 3352-3376.
    2. Michael Ostrovsky & Michael Schwarz, 2023. "Reserve Prices in Internet Advertising Auctions: A Field Experiment," Journal of Political Economy, University of Chicago Press, vol. 131(12), pages 3352-3376.
    3. Mahsa Derakhshan & Negin Golrezaei & Renato Paes Leme, 2022. "Linear Program-Based Approximation for Personalized Reserve Prices," Management Science, INFORMS, vol. 68(3), pages 1849-1864, March.
    4. W. Jason Choi & Amin Sayedi, 2019. "Learning in Online Advertising," Marketing Science, INFORMS, vol. 38(4), pages 584-608, July.
    5. Estrella Alonso & Joaquín Sánchez-Soriano & Juan Tejada, 2020. "Mixed Mechanisms for Auctioning Ranked Items," Mathematics, MDPI, vol. 8(12), pages 1-26, December.
    6. Gomes, Renato & Sweeney, Kane, 2014. "Bayes–Nash equilibria of the generalized second-price auction," Games and Economic Behavior, Elsevier, vol. 86(C), pages 421-437.
    7. Yang, Chaolin & Xiong, Yi, 2020. "Nonparametric advertising budget allocation with inventory constraint," European Journal of Operational Research, Elsevier, vol. 285(2), pages 631-641.
    8. N. Bora Keskin & Assaf Zeevi, 2017. "Chasing Demand: Learning and Earning in a Changing Environment," Mathematics of Operations Research, INFORMS, vol. 42(2), pages 277-307, May.
    9. Amine Allouah & Omar Besbes, 2020. "Prior-Independent Optimal Auctions," Management Science, INFORMS, vol. 66(10), pages 4417-4432, October.
    10. Yash Kanoria & Hamid Nazerzadeh, 2021. "Incentive-Compatible Learning of Reserve Prices for Repeated Auctions," Operations Research, INFORMS, vol. 69(2), pages 509-524, March.
    11. Hummel, Patrick, 2016. "Position auctions with dynamic resizing," International Journal of Industrial Organization, Elsevier, vol. 45(C), pages 38-46.
    12. Yi Zhu & Kenneth C. Wilbur, 2011. "Hybrid Advertising Auctions," Marketing Science, INFORMS, vol. 30(2), pages 249-273, 03-04.
    13. Hamid Nazerzadeh & Amin Saberi & Rakesh Vohra, 2013. "Dynamic Pay-Per-Action Mechanisms and Applications to Online Advertising," Operations Research, INFORMS, vol. 61(1), pages 98-111, February.
    14. Sentao Miao & Xi Chen & Xiuli Chao & Jiaxi Liu & Yidong Zhang, 2022. "Context‐based dynamic pricing with online clustering," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3559-3575, September.
    15. M. Yenmez, 2014. "Pricing in position auctions and online advertising," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 55(1), pages 243-256, January.
    16. Xiao, Baichun & Yang, Wei, 2021. "A Bayesian learning model for estimating unknown demand parameter in revenue management," European Journal of Operational Research, Elsevier, vol. 293(1), pages 248-262.
    17. Jehiel, Philippe & Lamy, Laurent, 2014. "On discrimination in procurement auctions," CEPR Discussion Papers 9790, C.E.P.R. Discussion Papers.
    18. Thomas Loots & Arnoud V. den Boer, 2023. "Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1169-1186, April.
    19. Francesco Decarolis & Maris Goldmanis & Antonio Penta, 2020. "Marketing Agencies and Collusive Bidding in Online Ad Auctions," Management Science, INFORMS, vol. 66(10), pages 4433-4454, October.
    20. Boxiao Chen & Xiuli Chao & Cong Shi, 2021. "Nonparametric Learning Algorithms for Joint Pricing and Inventory Control with Lost Sales and Censored Demand," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 726-756, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:282:y:2020:i:2:p:696-711. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.