Expert ranking method based on ListNet with multiple features

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 increased 14.2% comparing with the pairwise method with expert ranking.