论文标题
针对个性化建议的偏度排名优化
Skewness Ranking Optimization for Personalized Recommendation
论文作者
论文摘要
在本文中，我们提出了一个新颖的优化标准，该标准利用偏差正态分布的特征，以更好地模拟个性化建议的问题。具体而言，开发的标准借用了偏斜正态分布的概念和灵活性，这是基于三个超参数附加到优化标准的。此外，从理论的角度来看，我们不仅建立了偏斜正态分布中提出的标准的最大化与形状参数之间的关系，而且还提供了对ROC曲线下区域最大化的类比和渐近分析。在一系列大型现实世界数据集上进行的实验结果表明，我们的模型在所有测试的数据集中都显着优于最新的最佳性能。
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.
