A PSO-based algorithm for reserve price optimization in online ad auctions

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

One of the main mechanisms that online publishers use in online advertising in order to sell their advertisement space is the real-time bidding (RTB) mechanism. In RTB the publisher sells advertisement space via a second-price auction. Publishers can set a reserve price for their inventory in the second-price auction. In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method leverages the dynamics of particles in particle swarm optimization (PSO) to set reserve prices and is suitable for non-stationary environments. We also show that, taking the gap between the winning bid and second highest bid into account leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.
LanguageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages2611-2619
DOIs
StatePublished - 2019
Event2019 IEEE Congress on Evolutionary Computation - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019
http://cec2019.org/

Conference

Conference2019 IEEE Congress on Evolutionary Computation
CountryNew Zealand
CityWellington
Period10/06/1913/06/19
Internet address

Fingerprint

Particle swarm optimization (PSO)
Marketing
Experiments

Cite this

Rhuggenaath, J., Akcay, A., Zhang, Y., & Kaymak, U. (2019). A PSO-based algorithm for reserve price optimization in online ad auctions. In 2019 IEEE Congress on Evolutionary Computation (CEC) (pp. 2611-2619). [8789915] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/CEC.2019.8789915
Rhuggenaath, Jason ; Akcay, Alp ; Zhang, Yingqian ; Kaymak, Uzay. / A PSO-based algorithm for reserve price optimization in online ad auctions. 2019 IEEE Congress on Evolutionary Computation (CEC). Piscataway : Institute of Electrical and Electronics Engineers, 2019. pp. 2611-2619
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title = "A PSO-based algorithm for reserve price optimization in online ad auctions",
abstract = "One of the main mechanisms that online publishers use in online advertising in order to sell their advertisement space is the real-time bidding (RTB) mechanism. In RTB the publisher sells advertisement space via a second-price auction. Publishers can set a reserve price for their inventory in the second-price auction. In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method leverages the dynamics of particles in particle swarm optimization (PSO) to set reserve prices and is suitable for non-stationary environments. We also show that, taking the gap between the winning bid and second highest bid into account leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.",
author = "Jason Rhuggenaath and Alp Akcay and Yingqian Zhang and Uzay Kaymak",
year = "2019",
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Rhuggenaath, J, Akcay, A, Zhang, Y & Kaymak, U 2019, A PSO-based algorithm for reserve price optimization in online ad auctions. in 2019 IEEE Congress on Evolutionary Computation (CEC)., 8789915, Institute of Electrical and Electronics Engineers, Piscataway, pp. 2611-2619, 2019 IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10/06/19. DOI: 10.1109/CEC.2019.8789915

A PSO-based algorithm for reserve price optimization in online ad auctions. / Rhuggenaath, Jason; Akcay, Alp; Zhang, Yingqian; Kaymak, Uzay.

2019 IEEE Congress on Evolutionary Computation (CEC). Piscataway : Institute of Electrical and Electronics Engineers, 2019. p. 2611-2619 8789915.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AB - One of the main mechanisms that online publishers use in online advertising in order to sell their advertisement space is the real-time bidding (RTB) mechanism. In RTB the publisher sells advertisement space via a second-price auction. Publishers can set a reserve price for their inventory in the second-price auction. In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method leverages the dynamics of particles in particle swarm optimization (PSO) to set reserve prices and is suitable for non-stationary environments. We also show that, taking the gap between the winning bid and second highest bid into account leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.

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Rhuggenaath J, Akcay A, Zhang Y, Kaymak U. A PSO-based algorithm for reserve price optimization in online ad auctions. In 2019 IEEE Congress on Evolutionary Computation (CEC). Piscataway: Institute of Electrical and Electronics Engineers. 2019. p. 2611-2619. 8789915. Available from, DOI: 10.1109/CEC.2019.8789915