Expert list-wise ranking method based on sparse learning

Expert ranking is the core issue of expert retrieval. Taking into consideration the complexity of feature redundancy in traditional dense listwise Learning to Rank method and local optimum in parameter learning, the article proposed the expert listwise Learning to Rank method based on sparse learning. The objective function was defined through the optimization process of experts listwise ranking performance index. Then the Learning to Rank loss function was solved by the objective function. Thus feature dimension reduction was achieved by the feature threshold from the loss-control function of sparse learning algorithm and the steps above. In order to verify whether the feature threshold is optimal, the article made cross validation with the feature threshold and the objective function of model parameter vector to get the optimal model parameters vector and to verify the feature threshold. Meanwhile the article realized expert ranking via the expert listwise ranking model based on sparse learning, which depends on feature dimension reduction and parameter tuning. At last, the contrast experiments of expert ranking proved the effectiveness of the proposed method, which supported expert listwise ranking strongly.