Expert ranking method based on ListNet with multiple features

被引：3
|
作者：
陈方琼 [1 ,2 ]
余正涛 [1 ,2 ]
毛存礼 [1 ,2 ]
吴则键 [1 ,2 ]
张优敏 [1 ,2 ]
机构：
[1] School of Information Engineering and Automation,Kunming University of Science and Technology
[2] Intelligent Information Processing Key LaboratoryKunming University of Science and Technology
来源：
Journal of Beijing Institute of Technology | 2014年 / 23卷 / 02期
基金：
中国国家自然科学基金;
关键词：
expert retrieval; expert ranking; ListNet; multiple features;
D O I：
10.15918/j.jbit1004-0579.2014.02.018
中图分类号：
TP391.3 [检索机];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：240 / 247
页数：8
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