Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online
advertising industry, shifted from second to first price auctions. Due to the
fundamental difference between these auctions, demand-side platforms (DSPs)
have had to update their bidding strategies to avoid bidding unnecessarily high
and hence overpaying. Bid shading was proposed to adjust the bid price intended
for second-price auctions, in order to balance cost and winning probability in
a first-price auction setup. In this study, we introduce a novel deep
distribution network for optimal bidding in both open (non-censored) and closed
(censored) online first-price auctions. Offline and online A/B testing results
show that our algorithm outperforms previous state-of-art algorithms in terms
of both surplus and effective cost per action (eCPX) metrics. Furthermore, the
algorithm is optimized in run-time and has been deployed into VerizonMedia DSP
as production algorithm, serving hundreds of billions of bid requests per day.
Online A/B test shows that advertiser's ROI are improved by +2.4%, +2.4%, and
+8.6% for impression based (CPM), click based (CPC), and conversion based (CPA)
campaigns respectively.

本研究介绍了一种新型深度分配网络用于在线广告竞价优化，通过线上离线 A/B 测试以及生产线上环境使用，该方法在竞价成本和 ROI 方面显著优于之前的算法，并在 VerizonMedia DSP 上服务于每天数百亿的广告请求。