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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Information sciences Jg. 467; S. 342 - 356
Hauptverfasser:	Du, Lei, Pan, Yan, Ding, Jintang, Lai, Hanjiang, Huang, Changqin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.10.2018
Schlagworte:	Exponentiated gradient Learning to rank Sparse model Exponentiated gradient Sparse model Learning to rank
ISSN:	0020-0255, 1872-6291
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!