21. A system for learning ranking functions to determine the ranking of one or more content items that are responsive to a query, the system comprising: a computer network; a search engine operative to receive a search query comprising one or more terms from a user and to locate and retrieve one or more content items and related information responsive to the search query; an indexing component operative to parse the search query into one or more constituent terms and generate an index that defines a structure for the content items and related information that allows for location and retrieval of the content items and related information; an index data store operative to store the one or more indexes generated by the indexing component; a persistent data store operative to store a historical log of queries that users submit; and a ranking engine operative to, generate one or more training sets comprising one or more content item-query pairs; determine one or more contradicting pairs in a given training sets; formulate an optimization function to minimize the number of contradicting pairs in the training set using a functional iterative method that comprises applying an isotonic regression function within each query and using the output to determine regression targets for each content item-query pair in a next iteration; modify the optimization function by incorporating a grade difference between one or more content items corresponding to the query in the training set; apply the optimization function to each query in the training set; determine a ranking function based on the an application of regression trees on the one or more queries of the training set minimized by the optimization function; and store the ranking function for application to content item-query pairs not contained in the one or more training sets.
23. The system of claim 21 , wherein the ranking engine is operative to extract query dependent features for the one or more content item-query pairs.