Page Ranking Based on the Optimization of the Document's Relevance Class
Uses the machine learning methods to automatically build a ranking model,just like the widely used Pairwise approach.Based on the Pairwise approach,the new approach is equal for each query.It uses the cross-entropy to calculation the loss and selects the Top N related documents to expand the training set to increase the document distinction and reduce the noise impact of the irrelevant documents so that can improve the performance of the ranking approach.Experimental studies were conducted using the LETOR 4.0 data set which improved the ranking accuracies and demonstrated the effectiveness of the proposed method.