1. A method of determining a relevance function, comprising: determining one or more features to be used as predictor variables in the construction of a relevance function using at least one processor; parameterizing the relevance function by one or more coefficients; defining a query error that measures, for a given query, a difference between a ranking generated by the relevance function and a ranking based on a training set, the defined query error being a continuous function of coefficients, wherein said defining comprises defining the query error as a difference between an optimal discounted cumulative gain (DCG) function and an approximated version of a DCG; and determining values for the coefficients of the relevance function that substantially minimize an objective function that depends on the defined query error.
4. The method of claim 1 , wherein said defining the query error as a difference between an optimal DCG function and an approximated version of a DCG comprises: defining the query error as max π ⁢ ∑ i ∈ U q ⁢ D ⁡ ( π ⁡ ( i ) ) ⁢ G ⁡ ( y i ) - ∑ j = 1 ND q ⁢ D ⁡ ( j ) ⁢ ∑ i ∈ U q ⁢ G ⁡ ( y i ) ⁢ h ij ∑ i ∈ U q ⁢ h ij where D(j)=a discount function, NDq=a number of documents in the set of documents for the query, Uq=the set of documents for the query, π=a permutation from Uq to 1-NDq, y i =a training relevance for document i, G(y i )=a mapping function for y i , and h ij =a continuous version of an indicator function where document i is ranked in a position j by the relevance function.