基于似然损失函数的组样本排序学习方法

Group Sample Learning to Rank Approach Based on Likelihood Loss Function

组样本用于模型训练,为排序学习方法的构造提供一种新的思路.文中改进已有的组样本排序学习方法,构造组样本损失函数,用于排序学习模型的训练.基于似然损失函数,采用样本偏序权重损失函数和最优初始序列选择方法,构造基于神经网络的组排序学习方法,实验证明文中方法能够有效提高排序准确率.

Group sample used for training the ranking model provides a new idea to construct learning to rank methods. In this paper, the new loss function is constructed for group samples to train the learning to rank model. The preference-weighted loss function and the initial ranking list optimization are employed to construct a new group learning to rank method based on neural network. Experimental results show that the proposed approach is effective in improving ranking performance.