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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Editorials and Announcements

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• eBooks of issues of TC can now be downloaded from the Computer Society Digital Library
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New EssentialSet

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Editorials

• State of the Journal by Albert Y. Zomaya (Jan 2012)
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  Submission Deadline: September 15, 2013

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