1. A method to perform preference learning on a set of documents, comprising: receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters W; applying W to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating W; determining one or more sparse regularizing terms and updating W; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query, wherein said regularizing terms are responsive to an imposing of an entry-wise l 1 regularization on W and a refitting without the regularization is used to improve preference prediction while keeping the learned sparsity and said refitting reducing additional bias introduced by the l 1 regularization, said refitting being affected by P Ω ⁡ ( W ) ij = { W ij ⁢ ⁢ if ⁢ ⁢ ( i , j ) ∈ Ω 0 ⁢ ⁢ if ⁢ ⁢ ( i , j ) ⁢ ⁢ not ∈ Ω where W represents a relationship between a pair of words, Ω represents indices of non-zero entries of sparse W, and ij are matrix iterations.
1. A method to perform preference learning on a set of documents, comprising: receiving raw input features from the set of documents stored on a data storage device; generating polynomial combinations from the raw input features; generating one or more parameters W; applying W to one or more classifiers to generate outputs; determining a loss function and parameter gradients and updating W; determining one or more sparse regularizing terms and updating W; and expressing that one document is preferred over another in a search query and retrieving one or more documents responsive to the search query, wherein said regularizing terms are responsive to an imposing of an entry-wise l 1 regularization on W and a refitting without the regularization is used to improve preference prediction while keeping the learned sparsity and said refitting reducing additional bias introduced by the l 1 regularization, said refitting being affected by P Ω ⁡ ( W ) ij = { W ij ⁢ ⁢ if ⁢ ⁢ ( i , j ) ∈ Ω 0 ⁢ ⁢ if ⁢ ⁢ ( i , j ) ⁢ ⁢ not ∈ Ω where W represents a relationship between a pair of words, Ω represents indices of non-zero entries of sparse W, and ij are matrix iterations. 4. The method of claim 1 , comprising performing a stochastic (sub)gradient descent (SGD) with an online learning framework.