基于中标率估计和盈余最大化的投标报价遮蔽
Bid Shading by Win-Rate Estimation and Surplus Maximization
摘要：
本文描述了一种新的基于胜率的投标着色算法（WR），该算法不依赖于来自卖方平台（SSP）的最小投标获胜反馈。该方法使用修改后的逻辑回归来预测每个可能的阴影投标价格的利润。函数形式允许在运行时快速最大化，这是实时投标（RTB）系统的关键要求。我们报告的生产结果，从这种方法以及其他几种算法。我们发现，一般来说，出价着色可以为广告商提供显着的价值，将每次展示的价格降低到未着色成本的55%左右。此外，本文中描述的特定方法为广告商捕获了7%的利润，而不是仅仅出价最有可能获胜的价格的基准方法。我们还报告了比行业卖方平台遮阳服务高4.3%的盈余。此外，当该算法与预算控制器集成时，我们观察到eCPM，eCPC和eCPA降低了3% - 7%。我们将上述收益归因于盈余函数的显式最大化，并注意到其他算法可以利用这种相同的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
