An expert ranking method based on listnet with feature hierarchy
Shengxiang Gao, Zhengtao Yu, Sichao Wei, Yuan Yin, Yantuan Xian · 2016
Aiming to that the different hierarchic feature has a different degree contribution on expert ranking, the thesis proposes an expert ranking method which is based on ListNet combined with feature hierarchy type information. Firstly, the method thoroughly analyses characteristics of experts ranking, and defines four feature types, correlative features between query and document, page content features, language model features and expert-associated features. Then, considering the different feature type has a different degree contribution for expert ranking, according to the size of the contribution degree, it is defined a specific feature hierarchy value to each feature type. Finally, the feature hierarchy type values are combined together with the features defined by the first step to supervise expert ranking in ListNet algorithm. The experiments show that the introduction of feature hierarchy improves expert ranking quality.