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. 4. The method of claim 1 , wherein the maximizing the sum of the importance scores of the individual features and the minimizing the sum of the similarity scores between the selected features include: max ⁢ ⁢ ∑ i ⁢ ω i ⁢ x i min ⁢ ⁢ ∑ i ⁢ ⁢ ∑ j ≠ i ⁢ ⁢ e i , j ⁢ x i ⁢ x j s . t . ⁢ x i ∈ { 0 , 1 } ⁢ ⁢ i = 1 , … ⁢ , m ∑ i ⁢ ⁢ x i = t wherein t is a number of selected features; wherein x i =1 (or 0) denotes whether feature v i is selected or not; wherein ω i is an importance score of feature ν i ; wherein e i,t is an edge that denotes a similarity between the features ν i and ν j ; wherein e i,t=τ(ν νν j ).;and wherein e i,j=e j,i .