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Budget optimization in search-based advertising auctions

Published: 11 June 2007 Publication History

Abstract

Internet search companies sell advertisement slots based on users' search queries via an auction. While there has been previous work onthe auction process and its game-theoretic aspects, most of it focuses on the Internet company. In this work, we focus on the advertisers, who must solve a complex optimization problem to decide how to place bids on keywords to maximize their return (the number of user clicks on their ads) for a given budget. We model the entire process and study this budget optimization problem. While most variants are NP-hard, we show, perhaps surprisingly, that simply randomizing between two uniform strategies that bid equally on all the keywordsworks well. More precisely, this strategy gets at least a 1-1/ε fraction of the maximum clicks possible. As our preliminary experiments show, such uniform strategies are likely to be practical. We also present inapproximability results, and optimal algorithms for variants of the budget optimization problem.

References

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G. Aggarwal, A. Goel and R. Motwani. Truthful auctions for pricing search keywords. ACM Conference on Electronic Commerce, 1--7, 2006.
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G. Aggarwal, J. Feldman and S. Muthukrishnan Bidding to the Top: VCG and Equilibria of Position-Based Auctions Proc. WAOA, 2006.
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C. Borgs, J. Chayes, O. Etesami, N. Immorlica, K. Jain, and M. Mahdian. Dynamics of bid optimization in online advertisement auctions. Proc. WWW 2007.
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E. Clarke. Multipart pricing of public goods. Public Choice, 11(1):17--33, 1971.
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B. Edelman, M. Ostrovsky and M. Schwarz. Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords. Second workshop on sponsored search auctions, 2006.
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S. Muthukrishnan, M. Pál and Z. Svitkina. Stochastic models for budget optimization in search-based advertising. To appear in 3rd Workshop on Sponsored Search Auctions, WWW 2007.
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  • (2024)A field guide for pacing budget and ROS constraintsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692175(2607-2638)Online publication date: 21-Jul-2024
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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
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2007

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Author Tags

  1. auctions
  2. bidding
  3. optimization
  4. sponsored search

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EC07
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EC07: ACM Conference on Electronic Commerce
June 11 - 15, 2007
California, San Diego, USA

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

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Cited By

View all
  • (2024)Causal inference from competing treatmentsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693969(46657-46691)Online publication date: 21-Jul-2024
  • (2024)Factored-reward bandits with intermediate observationsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693569(36911-36952)Online publication date: 21-Jul-2024
  • (2024)A field guide for pacing budget and ROS constraintsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692175(2607-2638)Online publication date: 21-Jul-2024
  • (2024)Complex Dynamics in Autobidding SystemsProceedings of the 25th ACM Conference on Economics and Computation10.1145/3670865.3673551(75-100)Online publication date: 8-Jul-2024
  • (2024)Incentive Mechanism Design for ROI-Constrained Auto-biddingPRICAI 2024: Trends in Artificial Intelligence10.1007/978-981-96-0125-7_24(291-296)Online publication date: 12-Nov-2024
  • (2023)Delivery Optimized Discovery in Behavioral User Segmentation under Budget ConstraintProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614839(359-368)Online publication date: 21-Oct-2023
  • (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
  • (2023)Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of RandomizationProceedings of the ACM Web Conference 202310.1145/3543507.3583492(3561-3571)Online publication date: 30-Apr-2023
  • (2023)Auctions without commitment in the auto-bidding worldProceedings of the ACM Web Conference 202310.1145/3543507.3583416(3478-3488)Online publication date: 30-Apr-2023
  • (2023)Autobidding Auctions in the Presence of User CostsProceedings of the ACM Web Conference 202310.1145/3543507.3583234(3428-3435)Online publication date: 30-Apr-2023
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