From the June 2013 issue
Sparse Learning-to-Rank via an Efficient Primal-Dual Algorithm
By Hanjiang Lai, Yan Pan, Cong Liu, Liang Lin, & Jie Wu
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 optimization problem with a sparse-inducing ℓ1 constraint. Since the ℓ1 constraint is nondifferentiable, the critical issue arising here is how to efficiently solve the optimization problem. To address this issue, we propose a learning algorithm from the primal dual perspective.
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