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Learning to Rank (LtR) is an effective machine learning methodology for inducing high-quality document ranking functions. Given a query and a candidate set of documents, where query-document pairs are represented by feature vectors, a machine-learned function is used to reorder this set. In this paper we propose a new family of rank-based features, which extend the original feature vector associated with each query-document pair. Indeed, since they are derived as a function of thedoi:10.1145/2766462.2767776 dblp:conf/sigir/LuccheseNOPT15 fatcat:4i322gmdxfen5hwimnyes76cj4