1. A method in a computer system for providing improved search results in response to a user query, the method comprising: obtaining a set of ranked query item pairs, each query item pair corresponding to a query and a hit document; for each hit document: executing a series of query relaxation operations, each query relaxation operation extracting one feature vector of a plurality of feature vectors for said each hit document; and calculating a relevance score using the one extracted feature vector and a learned ranking function that is unique for that query relaxation operation; and generating a hit list for the user query to be displayed to the user that contains hits from the executed query relaxation operations; modeling a ranked item as a pair comprised of a query and a hit document and only items that have the same query, wherein each item is represented by a feature vector of the plurality of feature vectors, which lists features and corresponding feature weights configured to provide a user to tune a ranking function based on coupling a query relaxation method with a ranking support vector machine (SVM) application, and wherein the user determines an optimal fit between an initial list of document query hits and a revised list produced from a trained learning system machine learning function and a feature includes any attribute of a document that is used to determine a relevance of a document related to a given query.