Sparse Learning-to-Rank via an Efficient Primal-Dual Algorithm

Abstract

Learning-to-rank for information retrieval has gained increasing interest in recent years. Inspired by the success of sparse models, we consider the problem of sparse learning-to-rank, where the learned ranking models are constrained to be with only a few nonzero coefficients. We begin by formulating the sparse learning-to-rank problem as a convex… (More)
DOI: 10.1109/TC.2012.62

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