In the e-commerce advertising scenario, estimating the true probabilities
(known as a calibrated estimate) on CTR and CVR is critical and can directly
affect the benefits of the buyer, seller and platform. Previous research has
introduced numerous solutions for addressing the calibration problem. These
methods typically involve the training of calibrators using a validation set
and subsequently applying these calibrators to correct the original estimated
values during online inference. However, what sets e-commerce advertising
scenarios is the challenge of multi-field calibration. Multi-field calibration
can be subdivided into two distinct sub-problems: value calibration and shape
calibration. Value calibration is defined as no over- or under-estimation for
each value under concerned fields. Shape calibration is defined as no over- or
under-estimation for each subset of the pCTR within the specified range under
condition of concerned fields. In order to achieve shape calibration and value
calibration, it is necessary to have a strong data utilization ability.Because
the quantity of pCTR specified range for single field-value sample is relative
small, which makes the calibrator more difficult to train. However the existing
methods cannot simultaneously fulfill both value calibration and shape
calibration. To solve these problems, we propose a new method named Deep
Ensemble Shape Calibration (DESC). We introduce innovative basis calibration
functions, which enhance both function expression capabilities and data
utilization by combining these basis calibration functions. A significant
advancement lies in the development of an allocator capable of allocating the
most suitable shape calibrators to different estimation error distributions
within diverse fields and values.

在电子商务广告中，准确估计点击率（CTR）和转化率（CVR）的真实概率（称为校准估计）对买家、卖家和平台的利益至关重要。本研究提出了一种名为深度集成形状校准（DESC）的新方法，通过结合基础校准函数增强函数表达能力和数据利用能力，解决了多场景校准中的数值校准和形状校准的问题。

深度集成形状校准：在线广告中的多场后处理校准

Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in  Online Advertising

In e-commerce advertising, it is crucial to jointly consider various
performance metrics, e.g., user experience, advertiser utility, and platform
revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be
suboptimal due to their fixed allocation rules to optimize a single performance
metric (e.g., revenue or social welfare). Recently, data-driven auctions,
learned directly from auction outcomes to optimize multiple performance
metrics, have attracted increasing research interests. However, the procedure
of auction mechanisms involves various discrete calculation operations, making
it challenging to be compatible with continuous optimization pipelines in
machine learning. In this paper, we design \underline{D}eep \underline{N}eural
\underline{A}uctions (DNAs) to enable end-to-end auction learning by proposing
a differentiable model to relax the discrete sorting operation, a key component
in auctions. We optimize the performance metrics by developing deep models to
efficiently extract contexts from auctions, providing rich features for auction
design. We further integrate the game theoretical conditions within the model
design, to guarantee the stability of the auctions. DNAs have been successfully
deployed in the e-commerce advertising system at Taobao. Experimental
evaluation results on both large-scale data set as well as online A/B test
demonstrated that DNAs significantly outperformed other mechanisms widely
adopted in industry.

该研究提出 Deep Neural Auctions (DNAs) 模型，通过设计可微分模型解决了拍卖机制中的离散排序操作问题，实现了端到端的拍卖学习，提高了基于电商广告的多种性能指标的效果。

神经拍卖：端到端学习用于电子商务广告拍卖的拍卖机制

Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce  Advertising

In e-commerce advertising, the ad platform usually relies on auction
mechanisms to optimize different performance metrics, such as user experience,
advertiser utility, and platform revenue. However, most of the state-of-the-art
auction mechanisms only focus on optimizing a single performance metric, e.g.,
either social welfare or revenue, and are not suitable for e-commerce
advertising with various, dynamic, difficult to estimate, and even conflicting
performance metrics. In this paper, we propose a new mechanism called Deep GSP
auction, which leverages deep learning to design new rank score functions
within the celebrated GSP auction framework. These new rank score functions are
implemented via deep neural network models under the constraints of monotone
allocation and smooth transition. The requirement of monotone allocation
ensures Deep GSP auction nice game theoretical properties, while the
requirement of smooth transition guarantees the advertiser utilities would not
fluctuate too much when the auction mechanism switches among candidate
mechanisms to achieve different optimization objectives. We deployed the
proposed mechanisms in a leading e-commerce ad platform and conducted
comprehensive experimental evaluations with both offline simulations and online
A/B tests. The results demonstrated the effectiveness of the Deep GSP auction
compared to the state-of-the-art auction mechanisms.

本文提出了一种名为 Deep GSP auction 的新拍卖机制，它利用深度学习在 GSP 拍卖框架下设计新的排名得分函数，实现了对多种、动态、难以估计、甚至矛盾的性能指标进行优化，并在领先的电子商务广告平台上进行了广泛的实验评估，证明了其效果优于现有的拍卖机制。