  Accurately predicting the probabilities of user feedback, such as clicks and
conversions, is critical for advertisement ranking and bidding. However, there
often exist unwanted mismatches between predicted probabilities and true
likelihoods due to the rapid shift of data distributions and intrinsic model
biases. Calibration aims to address this issue by post-processing model
predictions, and field-aware calibration can adjust model output on different
feature field values to satisfy fine-grained advertising demands.
Unfortunately, the observed samples corresponding to certain field values can
be seriously limited to make confident calibrations, which may yield bias
amplification and online disturbance. In this paper, we propose a
confidence-aware multi-field calibration method, which adaptively adjusts the
calibration intensity based on confidence levels derived from sample
statistics. It also utilizes multiple fields for joint model calibration
according to their importance to mitigate the impact of data sparsity on a
single field. Extensive offline and online experiments show the superiority of
our method in boosting advertising performance and reducing prediction
deviations.
