  In this paper, we propose a novel optimization criterion that leverages
features of the skew normal distribution to better model the problem of
personalized recommendation. Specifically, the developed criterion borrows the
concept and the flexibility of the skew normal distribution, based on which
three hyperparameters are attached to the optimization criterion. Furthermore,
from a theoretical point of view, we not only establish the relation between
the maximization of the proposed criterion and the shape parameter in the skew
normal distribution, but also provide the analogies and asymptotic analysis of
the proposed criterion to maximization of the area under the ROC curve.
Experimental results conducted on a range of large-scale real-world datasets
show that our model significantly outperforms the state of the art and yields
consistently best performance on all tested datasets.
