  Scale-calibrated ranking systems are ubiquitous in real-world applications
nowadays, which pursue accurate ranking quality and calibrated probabilistic
predictions simultaneously. For instance, in the advertising ranking system,
the predicted click-through rate (CTR) is utilized for ranking and required to
be calibrated for the downstream cost-per-click ads bidding. Recently,
multi-objective based methods have been wildly adopted as a standard approach
for Calibrated Ranking, which incorporates the combination of two loss
functions: a pointwise loss that focuses on calibrated absolute values and a
ranking loss that emphasizes relative orderings. However, when applied to
industrial online applications, existing multi-objective CR approaches still
suffer from two crucial limitations. First, previous methods need to aggregate
the full candidate list within a single mini-batch to compute the ranking loss.
Such aggregation strategy violates extensive data shuffling which has long been
proven beneficial for preventing overfitting, and thus degrades the training
effectiveness. Second, existing multi-objective methods apply the two
inherently conflicting loss functions on a single probabilistic prediction,
which results in a sub-optimal trade-off between calibration and ranking. To
tackle the two limitations, we propose a Self-Boosted framework for Calibrated
Ranking (SBCR).
