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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

1 Citaat (Scopus)

Uittreksel

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.
TaalEngels
Titel2019 IEEE Congress on Evolutionary Computation (CEC)
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's2611-2619
DOI's
StatusGepubliceerd - 2019
Evenement2019 IEEE Congress on Evolutionary Computation - Wellington, Nieuw-Zeeland
Duur: 10 jun 201913 jun 2019
http://cec2019.org/

Congres

Congres2019 IEEE Congress on Evolutionary Computation
LandNieuw-Zeeland
StadWellington
Periode10/06/1913/06/19
Internet adres

Vingerafdruk

Particle swarm optimization (PSO)
Marketing
Experiments

Citeer dit

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) (blz. 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. blz. 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.",
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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, blz. 2611-2619, Wellington, Nieuw-Zeeland, 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. blz. 2611-2619 8789915.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer 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. blz. 2611-2619. 8789915. Beschikbaar vanaf, DOI: 10.1109/CEC.2019.8789915