10. A computer-readable storage medium comprising computer-readable instructions executable on a computing device, the computer-readable instructions comprising: selecting features iteratively; defining an importance score for the features, wherein the importance score includes a ranking result; defining a similarity score between every two features based on the ranking result, wherein the similarity score is based on a correlation between ranking results of the two features; selecting the features that maximize total importance scores of the features and minimize total similarity scores of a set of features, wherein the features that are selected have a largest cumulative importance score and a least mutual similarity score; solving an optimization problem with the features that are selected for ranking; and using a greedy algorithm to solve the optimization problem, the greedy algorithm includes: constructing an undirected graph; 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.
10. A computer-readable storage medium comprising computer-readable instructions executable on a computing device, the computer-readable instructions comprising: selecting features iteratively; defining an importance score for the features, wherein the importance score includes a ranking result; defining a similarity score between every two features based on the ranking result, wherein the similarity score is based on a correlation between ranking results of the two features; selecting the features that maximize total importance scores of the features and minimize total similarity scores of a set of features, wherein the features that are selected have a largest cumulative importance score and a least mutual similarity score; solving an optimization problem with the features that are selected for ranking; and using a greedy algorithm to solve the optimization problem, the greedy algorithm includes: constructing an undirected graph; 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. 11. The computer-readable storage medium of claim 10 , wherein defining the importance score comprises at least one of a mean average precision (MAP), a normalized discounted cumulative gain (NDCG), or a loss function.