Arsm Gradient Estimator for Supervised Learning to Rank
Arsm Gradient Estimator for Supervised Learning to Rank
复制标题
DOI：
10.1109/icassp40776.2020.9053127
复制
发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
Siamak Zamani Dadaneh;Shahin Boluki;Mingyuan Zhou;Xiaoning Qian
中科院分区：
文献类型：
--
作者：
Siamak Zamani Dadaneh;Shahin Boluki;Mingyuan Zhou;Xiaoning Qian
我们提出了一个新的监督学习模型来排名。在我们的模型中，相关性标签被假设为遵循分类分布，其概率是基于评分函数构建的。我们优化训练目标的多变量分类变量与无偏和低方差梯度估计。学习排序方法通常可以分为逐点、成对和列表方法。虽然我们的评分函数是逐点的，建议的框架允许灵活性的损失函数的选择。在我们的新模型中，损失函数不需要是可微的，可以是逐点的或列表的。我们提出的方法在两个数据集上取得了更好或可比的结果相比，现有的成对和列表方法。
We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respect to the multivariate categorical variables with an unbiased and low-variance gradient estimator. Learning-to-rank methods can generally be categorized into pointwise, pairwise, and listwise approaches. Although our scoring function is pointwise, the proposed framework permits flexibility over the choice of the loss function. In our new model, the loss function need not be differentiable and can either be pointwise or listwise. Our proposed method achieves better or comparable results on two datasets compared with existing pairwise and listwise methods.