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In the e-commerce advertising scenario, estimating the true probabilities(known as a calibrated estimate) on CTR and CVR is critical and can directlyaffect the benefits of the buyer, seller and platform. Previous research hasintroduced numerous solutions for addressing the calibration problem. Thesemethods typically involve the training of calibrators using a validation setand subsequently applying these calibrators to correct the original estimatedvalues during online inference. However, what sets e-commerce advertisingscenarios is the challenge of multi-field calibration. Multi-field calibrationcan be subdivided into two distinct sub-problems: value calibration and shapecalibration. Value calibration is defined as no over- or under-estimation foreach value under concerned fields. Shape calibration is defined as no over- orunder-estimation for each subset of the pCTR within the specified range undercondition of concerned fields. In order to achieve shape calibration and valuecalibration, it is necessary to have a strong data utilization ability.Becausethe quantity of pCTR specified range for single field-value sample is relativesmall, which makes the calibrator more difficult to train. However the existingmethods cannot simultaneously fulfill both value calibration and shapecalibration. To solve these problems, we propose a new method named DeepEnsemble Shape Calibration (DESC). We introduce innovative basis calibrationfunctions, which enhance both function expression capabilities and datautilization by combining these basis calibration functions. A significantadvancement lies in the development of an allocator capable of allocating themost suitable shape calibrators to different estimation error distributionswithin diverse fields and values.

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