Abstract
| Language | English |
|---|---|
| Title of host publication | 2019 IEEE Congress on Evolutionary Computation (CEC) |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 2611-2619 |
| DOIs | |
| State | Published - 2019 |
| Event | 2019 IEEE Congress on Evolutionary Computation - Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019 http://cec2019.org/ |
Conference
| Conference | 2019 IEEE Congress on Evolutionary Computation |
|---|---|
| Country | New Zealand |
| City | Wellington |
| Period | 10/06/19 → 13/06/19 |
| Internet address |
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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 proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - A PSO-based algorithm for reserve price optimization in online ad auctions
AU - Rhuggenaath,Jason
AU - Akcay,Alp
AU - Zhang,Yingqian
AU - Kaymak,Uzay
PY - 2019
Y1 - 2019
N2 - 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.
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.
U2 - 10.1109/CEC.2019.8789915
DO - 10.1109/CEC.2019.8789915
M3 - Conference contribution
SP - 2611
EP - 2619
BT - 2019 IEEE Congress on Evolutionary Computation (CEC)
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway
ER -