论文标题
以抽样的速度学习到秩：Plackett-luce梯度估计，计算复杂性最小
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity
论文作者
论文摘要
Plackett-luce梯度估计可以通过采样技术在可行的时间限制内优化随机排名模型。不幸的是，现有方法的计算复杂性与排名的长度，即排名截止或项目收集大小相比，计算复杂性并不能很好地扩展。在本文中，我们介绍了新型的PL-rank-3算法，该算法执行公正的梯度估计，其计算复杂性与最佳分类算法相当。结果，我们的新颖学习到级别的方法适用于任何在合理时间内可行的标准排序的情况。我们的实验结果表明，在优化所需的时间内没有任何损失。对于该领域而言，我们的贡献可能有可能允许将最新的学习对方法应用于比以前可行的更大的量表。
Plackett-Luce gradient estimation enables the optimization of stochastic ranking models within feasible time constraints through sampling techniques. Unfortunately, the computational complexity of existing methods does not scale well with the length of the rankings, i.e. the ranking cutoff, nor with the item collection size. In this paper, we introduce the novel PL-Rank-3 algorithm that performs unbiased gradient estimation with a computational complexity comparable to the best sorting algorithms. As a result, our novel learning-to-rank method is applicable in any scenario where standard sorting is feasible in reasonable time. Our experimental results indicate large gains in the time required for optimization, without any loss in performance. For the field, our contribution could potentially allow state-of-the-art learning-to-rank methods to be applied to much larger scales than previously feasible.
