Reserve Price Optimization for First Price Auctions in Display Advertising

Zhe Feng, Sebastien Lahaie, Jon Schneider, Jinchao Ye
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:3230-3239, 2021.

Abstract

The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing. In light of this, publishers need to re-evaluate and optimize their auction parameters, notably reserve prices. In this paper, we propose a gradient-based algorithm to adaptively update and optimize reserve prices based on estimates of bidders’ responsiveness to experimental shocks in reserves. Our key innovation is to draw on the inherent structure of the revenue objective in order to reduce the variance of gradient estimates and improve convergence rates in both theory and practice. We show that revenue in a first-price auction can be usefully decomposed into a \emph{demand} component and a \emph{bidding} component, and introduce techniques to reduce the variance of each component. We characterize the bias-variance trade-offs of these techniques and validate the performance of our proposed algorithm through experiments on synthetic data and real display ad auctions data from a major ad exchange.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-feng21b, title = {Reserve Price Optimization for First Price Auctions in Display Advertising}, author = {Feng, Zhe and Lahaie, Sebastien and Schneider, Jon and Ye, Jinchao}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {3230--3239}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/feng21b/feng21b.pdf}, url = {https://proceedings.mlr.press/v139/feng21b.html}, abstract = {The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing. In light of this, publishers need to re-evaluate and optimize their auction parameters, notably reserve prices. In this paper, we propose a gradient-based algorithm to adaptively update and optimize reserve prices based on estimates of bidders’ responsiveness to experimental shocks in reserves. Our key innovation is to draw on the inherent structure of the revenue objective in order to reduce the variance of gradient estimates and improve convergence rates in both theory and practice. We show that revenue in a first-price auction can be usefully decomposed into a \emph{demand} component and a \emph{bidding} component, and introduce techniques to reduce the variance of each component. We characterize the bias-variance trade-offs of these techniques and validate the performance of our proposed algorithm through experiments on synthetic data and real display ad auctions data from a major ad exchange.} }
Endnote
%0 Conference Paper %T Reserve Price Optimization for First Price Auctions in Display Advertising %A Zhe Feng %A Sebastien Lahaie %A Jon Schneider %A Jinchao Ye %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-feng21b %I PMLR %P 3230--3239 %U https://proceedings.mlr.press/v139/feng21b.html %V 139 %X The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing. In light of this, publishers need to re-evaluate and optimize their auction parameters, notably reserve prices. In this paper, we propose a gradient-based algorithm to adaptively update and optimize reserve prices based on estimates of bidders’ responsiveness to experimental shocks in reserves. Our key innovation is to draw on the inherent structure of the revenue objective in order to reduce the variance of gradient estimates and improve convergence rates in both theory and practice. We show that revenue in a first-price auction can be usefully decomposed into a \emph{demand} component and a \emph{bidding} component, and introduce techniques to reduce the variance of each component. We characterize the bias-variance trade-offs of these techniques and validate the performance of our proposed algorithm through experiments on synthetic data and real display ad auctions data from a major ad exchange.
APA
Feng, Z., Lahaie, S., Schneider, J. & Ye, J.. (2021). Reserve Price Optimization for First Price Auctions in Display Advertising. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:3230-3239 Available from https://proceedings.mlr.press/v139/feng21b.html.

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