This paper describes a new win-rate based bid shading algorithm (WR) that
does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform
(SSP). The method uses a modified logistic regression to predict the profit
from each possible shaded bid price. The function form allows fast maximization
at run-time, a key requirement for Real-Time Bidding (RTB) systems. We report
production results from this method along with several other algorithms. We
found that bid shading, in general, can deliver significant value to
advertisers, reducing price per impression to about 55% of the unshaded cost.
Further, the particular approach described in this paper captures 7% more
profit for advertisers, than do benchmark methods of just bidding the most
probable winning price. We also report 4.3% higher surplus than an industry
Sell-Side Platform shading service. Furthermore, we observed 3% - 7% lower
eCPM, eCPC and eCPA when the algorithm was integrated with budget controllers.
We attribute the gains above as being mainly due to the explicit maximization
of the surplus function, and note that other algorithms can take advantage of
this same approach.

本文描述了一种基于胜率的出价阴影算法 (WR)，使用修改的逻辑回归预测每种可能阴影出价价格的收益，可以快速最大化运行时效率，实现了比基准方法更高的广告主利润与更高的剩余价值，并可用于实时竞价系统。