  AUC is a common metric for evaluating the performance of a classifier.
However, most classifiers are trained with cross entropy, and it does not
optimize the AUC metric directly, which leaves a gap between the training and
evaluation stage. In this paper, we propose the PDAOM loss, a Personalized and
Differentiable AUC Optimization method with Maximum violation, which can be
directly applied when training a binary classifier and optimized with
gradient-based methods. Specifically, we construct the pairwise exponential
loss with difficult pair of positive and negative samples within sub-batches
grouped by user ID, aiming to guide the classifier to pay attention to the
relation between hard-distinguished pairs of opposite samples from the
perspective of independent users. Compared to the origin form of pairwise
exponential loss, the proposed PDAOM loss not only improves the AUC and GAUC
metrics in the offline evaluation, but also reduces the computation complexity
of the training objective. Furthermore, online evaluation of the PDAOM loss on
the 'Guess What You Like' feed recommendation application in Meituan manifests
1.40% increase in click count and 0.65% increase in order count compared to the
baseline model, which is a significant improvement in this well-developed
online life service recommendation system.
