1. A ranking system that provides improved rankings for items returned for a given query comprising: a processor; a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions to implement the system, including: an initial set of items for which an ordering thereof is desired; and a multiple nested ranking component that ranks the initial set of items by employing a plurality of ranking stages, wherein a first stage provides a first ranking of the initial set of items and each subsequent stage after the first stage ranks a highest ranked subset of the set of items from a previous ranking stage immediately preceding the subsequent ranking stage, wherein the number of items in the subset decreases at each stage, wherein each stage employs one of a plurality of trained ranking algorithms, wherein each subsequent stage employs a trained ranking algorithm from the plurality of trained ranking algorithms that is a more sophisticated ranking algorithm that is slower to rank items, and requires more processing time than a trained ranking algorithm employed by the previous ranking stage; and wherein 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 ).