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.
| Original language | English |
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| Title of host publication | 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 2611-2619 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781728121536 |
| DOIs | |
| Publication status | Published - Jun 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 |
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| Country/Territory | New Zealand |
| City | Wellington |
| Period | 10/06/19 → 13/06/19 |
| Internet address |