Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in
  Online Advertising
Shuai Yang Hao Yang Zhuang Zou Linhe Xu Shuo Yuan Yifan Zeng
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.