Skewness Ranking Optimization for Personalized Recommendation
Abstract
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.
Cite
@article{arxiv.2005.12971,
title = {Skewness Ranking Optimization for Personalized Recommendation},
author = {Chuan-Ju Wang and Yu-Neng Chuang and Chih-Ming Chen and Ming-Feng Tsai},
journal= {arXiv preprint arXiv:2005.12971},
year = {2020}
}
Comments
Accepted by UAI'20. The first two authors contributed equally to this work; author order was determined by seniority