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Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability.
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