Skewness Ranking Optimization for Personalized Recommendation
This work addresses personalized recommendation, a key problem in e-commerce and content platforms, with incremental improvements through a new statistical modeling approach.
The authors tackled the problem of personalized recommendation by proposing a novel optimization criterion based on the skew normal distribution, which significantly outperformed state-of-the-art methods on large-scale real-world datasets.
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.