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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Bibliographic Details

Published in:	Information sciences Vol. 467; pp. 342 - 356
Main Authors:	Du, Lei, Pan, Yan, Ding, Jintang, Lai, Hanjiang, Huang, Changqin
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.10.2018
Subjects:	Exponentiated gradient Learning to rank Sparse model Exponentiated gradient Sparse model Learning to rank
ISSN:	0020-0255, 1872-6291
Online Access:	Get full text
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