EGRank: An exponentiated gradient algorithm for sparse learning-to-rank

This paper focuses on the problem of sparse learning-to-rank, where the learned ranking models usually have very few non-zero coefficients. An exponential gradient algorithm is proposed to learn sparse models for learning-to-rank, which can be formulated as a convex optimization problem with the ℓ1...

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Vydané v:	Information sciences Ročník 467; s. 342 - 356
Hlavní autori:	Du, Lei, Pan, Yan, Ding, Jintang, Lai, Hanjiang, Huang, Changqin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.10.2018
Predmet:	Exponentiated gradient Learning to rank Sparse model Exponentiated gradient Sparse model Learning to rank
ISSN:	0020-0255, 1872-6291
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