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Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve

Authors:

Maja R. Rudolph,

Joseph G. Ellis,

David M. BleiAuthors Info & Claims

WWW '16: Proceedings of the 25th International Conference on World Wide Web

Pages 1113 - 1122

https://doi.org/10.1145/2872427.2883051

Published: 11 April 2016 Publication History

Abstract

Many online companies sell advertisement space in second-price auctions with reserve. In this paper, we develop a probabilistic method to learn a profitable strategy to set the reserve price. We use historical auction data with features to fit a predictor of the best reserve price. This problem is delicate - the structure of the auction is such that a reserve price set too high is much worse than a reserve price set too low. To address this we develop objective variables, an approach for combining probabilistic modeling with optimal decision-making. Objective variables are "hallucinated observations" that transform the revenue maximization task into a regularized maximum likelihood estimation problem, which we solve with the EM algorithm. This framework enables a variety of prediction mechanisms to set the reserve price. As examples, we study objective variable methods with regression, kernelized regression, and neural networks on simulated and real data. Our methods outperform previous approaches both in terms of scalability and profit.

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Cited By

Marshall G(2023)Identification in english auctions with shill biddingQuantitative Marketing and Economics10.1007/s11129-023-09274-922:2(193-222)Online publication date: 23-Nov-2023
https://doi.org/10.1007/s11129-023-09274-9
Coey DLarsen BSweeney KWaisman C(2021)Scalable Optimal Online AuctionsMarketing Science10.1287/mksc.2021.128340:4(593-618)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1287/mksc.2021.1283
Zhiyanov ADrutsa ADaumé HSingh A(2020)Bisection-based pricing for repeated contextual auctions against strategic buyerProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3526001(11469-11480)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3526001
Show More Cited By

Index Terms

Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Electronic commerce
        Online auctions

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '16: Proceedings of the 25th International Conference on World Wide Web

April 2016

1482 pages

ISBN:9781450341431

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright © 2016 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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NSF
ONR
DARPA

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WWW '16

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IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

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WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Marshall G(2023)Identification in english auctions with shill biddingQuantitative Marketing and Economics10.1007/s11129-023-09274-922:2(193-222)Online publication date: 23-Nov-2023
https://doi.org/10.1007/s11129-023-09274-9
Coey DLarsen BSweeney KWaisman C(2021)Scalable Optimal Online AuctionsMarketing Science10.1287/mksc.2021.128340:4(593-618)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1287/mksc.2021.1283
Zhiyanov ADrutsa ADaumé HSingh A(2020)Bisection-based pricing for repeated contextual auctions against strategic buyerProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3526001(11469-11480)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3526001
Drutsa ADaumé HSingh A(2020)Reserve pricing in repeated second-price auctions with strategic biddersProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525189(2678-2689)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525189
Drutsa ADaumé HSingh A(2020)Optimal non-parametric learning in repeated contextual auctions with strategic buyerProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525188(2668-2677)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525188
Croissant LAbeille MCalauzènes CDaumé HSingh A(2020)Real-time optimisation for online learning in auctionsProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525145(2217-2226)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525145
Shen WTang PWang XXu YYang XEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Learning to Design Coupons in Online Advertising MarketsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398905(1242-1250)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398905
Vanunts ADrutsa AWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Optimal pricing in repeated posted-price auctions with different patience of the seller and the buyerProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454372(941-953)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454372
Rhuggenaath JAkcay AZhang YKaymak U(2019)Optimizing reserve prices for publishers in online ad auctions2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)10.1109/CIFEr.2019.8759123(1-8)Online publication date: May-2019
https://doi.org/10.1109/CIFEr.2019.8759123
Rhuggenaath JAkcay AZhang YKaymak U(2019)A PSO-based Algorithm for Reserve Price Optimization in Online Ad Auctions2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8789915(2611-2619)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8789915
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