Online bidding and auction are crucial aspects of the online advertising
industry. Conventionally, there is only one slot for ad display and most
current studies focus on it. Nowadays, multi-slot display advertising is
gradually becoming popular where many ads could be displayed in a list and
shown as a whole to users. However, multi-slot display advertising leads to
different cost-effectiveness. Advertisers have the incentive to adjust bid
prices so as to win the most economical ad positions. In this study, we
introduce bid shading into multi-slot display advertising for bid price
adjustment with a Multi-task End-to-end Bid Shading(MEBS) method. We prove the
optimality of our method theoretically and examine its performance
experimentally. Through extensive offline and online experiments, we
demonstrate the effectiveness and efficiency of our method, and we obtain a
7.01% lift in Gross Merchandise Volume, a 7.42% lift in Return on Investment,
and a 3.26% lift in ad buy count.

在线竞价、拍卖、多槽位广告展示、出价调整以及成本效益性是本研究的主要关键词，通过引入多任务端到端出价调整方法 (MEBS)，我们在多槽位广告展示中证明了该方法的理论最优性，并通过离线和在线实验的广泛验证取得了 7.01% 的总商品交易额提升、7.42% 的投资回报率提升和 3.26% 的广告采购计数提升。

MEBS: 多任务多槽位展示广告的端到端出价优化

MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display  Advertising

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。

一种用于首价拍卖出价调整的高效深度分布式网络

An Efficient Deep Distribution Network for Bid Shading in First-Price  Auctions

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。