排序学习(learning to rank)是一种机器学习与信息检索的交叉学科,可以从大量的包含标记的训练集中自动学习排序模型。特征选取对于排序模型的预测结果有很大的影响,而排序学习对其特征领域的研究却很少。针对这一问题,提出一种特征处...排序学习(learning to rank)是一种机器学习与信息检索的交叉学科,可以从大量的包含标记的训练集中自动学习排序模型。特征选取对于排序模型的预测结果有很大的影响,而排序学习对其特征领域的研究却很少。针对这一问题,提出一种特征处理方法:利用基于主成分分析(PCA)的特征重组方法扩展数据集,然后在扩展后的数据集上进行排序算法隐含的特征选择。在LETOR4.0数据集(MQ2007,MQ2008)上基于排序评测函数对List Net排序算法进行验证。通过对比特征处理前后的排序性能差异,以及添加新特征的个数对排序结果的影响,实验结果表明,经过特征处理的利用排序学习算法构建的排序函数一般要优于原始的排序函数。展开更多
The quality of expert ranking directly affects the expert retrieval precision.According to the characteristics of the expert entity,an expert ranking model based on the list with multiple features was proposed.Firstly...The quality of expert ranking directly affects the expert retrieval precision.According to the characteristics of the expert entity,an expert ranking model based on the list with multiple features was proposed.Firstly,multiple features was selected through the analysis of expert pages;secondly,in order to learn parameters through gradient descent and construct expert ranking model,all features were integrated into ListNet ranking model;finally,expert ranking contrast experiment will be performed using the trained model.The experimental results show that the proposed method has a good effect,and the value of NDCG@1 increased14.2%comparing with the pairwise method with expert ranking.展开更多
文摘排序学习(learning to rank)是一种机器学习与信息检索的交叉学科,可以从大量的包含标记的训练集中自动学习排序模型。特征选取对于排序模型的预测结果有很大的影响,而排序学习对其特征领域的研究却很少。针对这一问题,提出一种特征处理方法:利用基于主成分分析(PCA)的特征重组方法扩展数据集,然后在扩展后的数据集上进行排序算法隐含的特征选择。在LETOR4.0数据集(MQ2007,MQ2008)上基于排序评测函数对List Net排序算法进行验证。通过对比特征处理前后的排序性能差异,以及添加新特征的个数对排序结果的影响,实验结果表明,经过特征处理的利用排序学习算法构建的排序函数一般要优于原始的排序函数。
基金Supported by the National Natural Science Foundation of China(61175068)
文摘The quality of expert ranking directly affects the expert retrieval precision.According to the characteristics of the expert entity,an expert ranking model based on the list with multiple features was proposed.Firstly,multiple features was selected through the analysis of expert pages;secondly,in order to learn parameters through gradient descent and construct expert ranking model,all features were integrated into ListNet ranking model;finally,expert ranking contrast experiment will be performed using the trained model.The experimental results show that the proposed method has a good effect,and the value of NDCG@1 increased14.2%comparing with the pairwise method with expert ranking.
