research-article

An empirical study of reserve price optimisation in real-time bidding

Authors:

Sam SeljanAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1897 - 1906

https://doi.org/10.1145/2623330.2623357

Published: 24 August 2014 Publication History

Abstract

In this paper, we report the first empirical study and live test of the reserve price optimisation problem in the context of Real-Time Bidding (RTB) display advertising from an operational environment. A reserve price is the minimum that the auctioneer would accept from bidders in auctions, and in a second price auction it could potentially uplift the auctioneer's revenue by charging winners the reserve price instead of the second highest bids. As such it has been used for sponsored search and been well studied in that context. However, comparing with sponsored search and contextual advertising, this problem in the RTB context is less understood yet more critical for publishers because 1) bidders have to submit a bid for each individual impression, which mostly is associated with user data that is subject to change over time. This, coupled with practical constraints such as the budget, campaigns' life time, etc. makes the theoretical result from optimal auction theory not necessarily applicable and a further empirical study is required to confirm its optimality from the real-world system; 2) in RTB an advertiser is facing nearly unlimited supply and the auction is almost done in "last second", which encourages spending less on the high cost ad placements. This could imply the loss of bid volume over time if a correct reserve price is not in place. In this paper we empirically examine several commonly adopted algorithms for setting up a reserve price. We report our results of a large scale online experiment in a production platform. The results suggest the our proposed game theory based OneShot algorithm performed the best and the superiority is significant in most cases.

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Cited By

Deng YGolrezaei NJaillet PLiang JMirrokni VChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Individual Welfare Guarantees in the Autobidding World with Machine-learned AdviceProceedings of the ACM Web Conference 202410.1145/3589334.3645660(267-275)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645660
Zhang CZhou JWang JFan JShi Y(2024)Fairness-Aware Competitive Bidding Influence Maximization in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.328560511:2(2147-2159)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3285605
Ihsan RKhurshid SShoaib MAli SMahnoor SHamza S(2024)A technique to forecast Pakistan’s news using deep hybrid learning modelInternational Journal of Information Technology10.1007/s41870-024-01781-616:4(2505-2516)Online publication date: 11-Mar-2024
https://doi.org/10.1007/s41870-024-01781-6
Show More Cited By

Index Terms

An empirical study of reserve price optimisation in real-time bidding
1. Information systems
  1. World Wide Web
    1. Online advertising
    2. Web applications
      1. Electronic commerce

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Publication History

Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Deng YGolrezaei NJaillet PLiang JMirrokni VChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Individual Welfare Guarantees in the Autobidding World with Machine-learned AdviceProceedings of the ACM Web Conference 202410.1145/3589334.3645660(267-275)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645660
Zhang CZhou JWang JFan JShi Y(2024)Fairness-Aware Competitive Bidding Influence Maximization in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.328560511:2(2147-2159)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3285605
Ihsan RKhurshid SShoaib MAli SMahnoor SHamza S(2024)A technique to forecast Pakistan’s news using deep hybrid learning modelInternational Journal of Information Technology10.1007/s41870-024-01781-616:4(2505-2516)Online publication date: 11-Mar-2024
https://doi.org/10.1007/s41870-024-01781-6
Ou WChen BDai XZhang WLiu WTang RYu Y(2023)A Survey on Bid Optimization in Real-Time Bidding Display AdvertisingACM Transactions on Knowledge Discovery from Data10.1145/362860318:3(1-31)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3628603
Ou WChen BLiu WDai XZhang WXia WLi XTang RYu YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Optimal Real-Time Bidding Strategy for Position Auctions in Online AdvertisingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614727(4766-4772)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614727
Kalra AWang CBorcea CChen Y(2023)Reserve Price optimization in First-Price Auctions via Multi-Task Learning2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00029(200-209)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00029
Zhong KMa YAngel SYin HStavrou ACremers CShi E(2022)IbexProceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security10.1145/3548606.3560651(3223-3237)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3548606.3560651
Zhang HZhang LHuang JLi ACheng HHuang DXu L(2022)A Unified Guaranteed Impression Allocation Framework for Online Display Advertising2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00079(686-694)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00079
Shi ZZhou Hde Laat CZhao Z(2022)A Bayesian game-enhanced auction model for federated cloud services using blockchainFuture Generation Computer Systems10.1016/j.future.2022.05.017136:C(49-66)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.future.2022.05.017
Afshar RZhang YKaymak U(2022)Dynamic Ad Network Ordering Method Using Reinforcement LearningInternational Journal of Computational Intelligence Systems10.1007/s44196-022-00077-615:1Online publication date: 19-Apr-2022
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