Article

Allocating online advertisement space with unreliable estimates

Authors:

Mohammad Mahdian,

Hamid Nazerzadeh,

Amin SaberiAuthors Info & Claims

EC '07: Proceedings of the 8th ACM conference on Electronic commerce

Pages 288 - 294

https://doi.org/10.1145/1250910.1250952

Published: 11 June 2007 Publication History

Abstract

We study the problem of optimally allocating online advertisement space to budget-constrained advertisers. This problem was defined and studied from the perspective of worst-case online competitive analysis by Mehta et al.

Our objective is to find an algorithm that takes advantage of the given estimates of the frequencies of keywords to compute a near optimal solution when the estimates are accurate, while at the same time maintaining a good worst-case competitive ratio in case the estimates are totally incorrect. This is motivated by real-world situations where search engines have stochastic information that provide reasonably accurate estimates of the frequency of search queries except in certain highly unpredictable yet economically valuable spikes in the search pattern.

Our approach is a black-box approach: we assume we have access to an oracle that uses the given estimates to recommend an advertiser everytime a query arrives. We use this oracle to design an algorithm that provides two performance guarantees: the performance guarantee in the case that the oracle gives an accurate estimate, and its worst-case performance guarantee. Our algorithm can be fine tuned by adjusting a parameter α, giving a tradeoff curve between the two performance measures with the best competitive ratio for the worst-case scenario at one end of the curve and the optimal solution for the scenario where estimates are accurate at the other en.

Finally, we demonstrate the applicability of our framework by applying it to two classical online problems, namely the lost cow and the ski rental problems.

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Cited By

Polak AZub M(2024)Learning-augmented maximum flowInformation Processing Letters10.1016/j.ipl.2024.106487186:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.ipl.2024.106487
Balkanski EPerivier NStein CWei HOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Energy-efficient scheduling with predictionsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669577(79012-79023)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669577
Spaeh FEne AOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Online ad allocation with predictionsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666878(17265-17295)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666878
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Index Terms

Allocating online advertisement space with unreliable estimates
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics
2. Theory of computation
  1. Design and analysis of algorithms

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Published In

cover image ACM Conferences

EC '07: Proceedings of the 8th ACM conference on Electronic commerce

June 2007

384 pages

ISBN:9781595936530

DOI:10.1145/1250910

General Chair:
Jeffrey MacKie-Mason
University of Michigan
,
Program Chairs:
David Parkes
Harvard University
,
Paul Resnick
University of Michigan

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 June 2007

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EC07: ACM Conference on Electronic Commerce

June 11 - 15, 2007

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

Polak AZub M(2024)Learning-augmented maximum flowInformation Processing Letters10.1016/j.ipl.2024.106487186:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.ipl.2024.106487
Balkanski EPerivier NStein CWei HOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Energy-efficient scheduling with predictionsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669577(79012-79023)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669577
Spaeh FEne AOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Online ad allocation with predictionsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666878(17265-17295)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666878
Mishra PMoustafa AIto T(2023)Real-time Pricing-based Resource Allocation in Open Market EnvironmentsACM Transactions on Internet Technology10.1145/346523723:1(1-22)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3465237
Argue CFrieze AGupta ASeiler CKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Learning from a sample in online algorithmsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601277(13852-13863)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601277
Zhu JLiu S(2022)An economic analysis of maximally representative allocationsApplied Economics10.1080/00036846.2022.208237054:59(6744-6754)Online publication date: 1-Sep-2022
https://doi.org/10.1080/00036846.2022.2082370
Ma WSimchi-Levi DZhao J(2021)Dynamic Pricing (and Assortment) Under a Static CalendarManagement Science10.1287/mnsc.2020.367167:4(2292-2313)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1287/mnsc.2020.3671
Liu YComden JLiu ZYang Y(2021)Online Resource Provisioning for Wireless Data CollectionACM Transactions on Sensor Networks10.1145/347064818:1(1-27)Online publication date: 5-Oct-2021
https://dl.acm.org/doi/10.1145/3470648
Cohen IWajc DCzumaj A(2018)Randomized online matching in regular graphsProceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms10.5555/3174304.3175332(960-979)Online publication date: 7-Jan-2018
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Devanur NSivan BSyrgkanis VCzumaj A(2018)Truthful multi-parameter auctions with online supplyProceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms10.5555/3174304.3175319(753-769)Online publication date: 7-Jan-2018
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