9. A method that provides improved rankings for items returned for a given query comprising: employing a processor executing computer executable instructions embodied on a computer readable storage medium to perform the following acts: retrieving an initial ranked ordered set of items, wherein each item is assigned a relevance value; and iteratively re-ranking, conducted via at least one trained ranking algorithm, one or more subsets of the set of items, wherein each subset comprises a plurality of ranked items from the previous iteration that are not below a threshold relevance value, the number of items in the subset decreases at each iteration, wherein the threshold relevance value is automatically selected by artificial intelligence based upon the number of items in the set of items, wherein each iteration employs a ranking algorithm that is slower to rank items than a ranking algorithm employed to rank items in a previous iteration; and wherein the at least one trained ranking algorithm includes a learning algorithm that is given a set of pairs of items [x i ,x j ] with probability P ij that item x i is to be ranked higher than item x j and a ranking model f, where f(x i )>f(x j ) indicates that the ranking model ranks x i higher than x j , P _ ij ≡ e o _ ij 1 + e o _ ij , where ranking model output ō ij ≡f(x i )−(x j ).
9. A method that provides improved rankings for items returned for a given query comprising: employing a processor executing computer executable instructions embodied on a computer readable storage medium to perform the following acts: retrieving an initial ranked ordered set of items, wherein each item is assigned a relevance value; and iteratively re-ranking, conducted via at least one trained ranking algorithm, one or more subsets of the set of items, wherein each subset comprises a plurality of ranked items from the previous iteration that are not below a threshold relevance value, the number of items in the subset decreases at each iteration, wherein the threshold relevance value is automatically selected by artificial intelligence based upon the number of items in the set of items, wherein each iteration employs a ranking algorithm that is slower to rank items than a ranking algorithm employed to rank items in a previous iteration; and wherein the at least one trained ranking algorithm includes a learning algorithm that is given a set of pairs of items [x i ,x j ] with probability P ij that item x i is to be ranked higher than item x j and a ranking model f, where f(x i )>f(x j ) indicates that the ranking model ranks x i higher than x j , P _ ij ≡ e o _ ij 1 + e o _ ij , where ranking model output ō ij ≡f(x i )−(x j ). 14. The method of claim 9 , the re-ranking employs a plurality of ranking algorithms, wherein each successive iteration employs a more complex ranking algorithm that achieves improved ranking.