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Recently increasing attention has been focused on directly optimizing ranking measures and inducing sparsity in learning models. However, few attempts have been made to relate them together in approaching the problem of learning to rank. In this paper, we consider the sparse algorithms to directly optimize the Normalized Discounted Cumulative Gain (NDCG) which is a widely-used ranking measure. We begin by establishing a reduction framework under which we reduce ranking, as measured by NDCG, todoi:10.1145/1571941.1571987 dblp:conf/sigir/SunQTW09 fatcat:nvfaso4ffjcqrnuyb5efyqxasi