1. A method to select features for ranking for information retrieval, implemented by a computing device, the method comprising: selecting the features iteratively; computing importance scores for the features, wherein the importance scores include ranking performances; measuring similarity scores between two features based on the ranking performances, wherein the similarity scores are based on a correlation between ranking results of the two features; and selecting the features that maximize a sum of the importance scores of individual features and that minimize a sum of the similarity scores between the selected features, wherein the features that are selected have a largest cumulative importance score and a least mutual similarity score; wherein the features selected for ranking are modeled as an optimization problem, the optimization problem includes: constructing an undirected graph and using a node to represent a feature; representing a weight of the node and a weight of an edge between two nodes; and constructing a set to contain the selected features.
1. A method to select features for ranking for information retrieval, implemented by a computing device, the method comprising: selecting the features iteratively; computing importance scores for the features, wherein the importance scores include ranking performances; measuring similarity scores between two features based on the ranking performances, wherein the similarity scores are based on a correlation between ranking results of the two features; and selecting the features that maximize a sum of the importance scores of individual features and that minimize a sum of the similarity scores between the selected features, wherein the features that are selected have a largest cumulative importance score and a least mutual similarity score; wherein the features selected for ranking are modeled as an optimization problem, the optimization problem includes: constructing an undirected graph and using a node to represent a feature; representing a weight of the node and a weight of an edge between two nodes; and constructing a set to contain the selected features. 2. The method of claim 1 , wherein computing the importance scores comprises at least one of a mean average precision (MAP), a normalized discounted cumulative gain (NDCG), or a loss function.