IEICE TRANSACTIONS on Information
Open Access
A Short Introduction to Learning to Rank
-
Full Text Views
33
- Cite this
- Free PDF (311KB)
Summary :
Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Intensive studies have been conducted on the problem and significant progress has been made [1],[2]. This short paper gives an introduction to learning to rank, and it specifically explains the fundamental problems, existing approaches, and future work of learning to rank. Several learning to rank methods using SVM techniques are described in details.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.10 pp.1854-1862
- Publication Date
- 2011/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1854
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Information-Based Induction Sciences and Machine Learning)
- Category
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Hang LI, "A Short Introduction to Learning to Rank" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 10, pp. 1854-1862, October 2011, doi: 10.1587/transinf.E94.D.1854.
Abstract: Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Intensive studies have been conducted on the problem and significant progress has been made [1],[2]. This short paper gives an introduction to learning to rank, and it specifically explains the fundamental problems, existing approaches, and future work of learning to rank. Several learning to rank methods using SVM techniques are described in details.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1854/_p
Copy
@ARTICLE{e94-d_10_1854,
author={Hang LI, },
journal={IEICE TRANSACTIONS on Information},
title={A Short Introduction to Learning to Rank},
year={2011},
volume={E94-D},
number={10},
pages={1854-1862},
abstract={Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Intensive studies have been conducted on the problem and significant progress has been made [1],[2]. This short paper gives an introduction to learning to rank, and it specifically explains the fundamental problems, existing approaches, and future work of learning to rank. Several learning to rank methods using SVM techniques are described in details.},
keywords={},
doi={10.1587/transinf.E94.D.1854},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - A Short Introduction to Learning to Rank
T2 - IEICE TRANSACTIONS on Information
SP - 1854
EP - 1862
AU - Hang LI
PY - 2011
DO - 10.1587/transinf.E94.D.1854
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2011
AB - Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Intensive studies have been conducted on the problem and significant progress has been made [1],[2]. This short paper gives an introduction to learning to rank, and it specifically explains the fundamental problems, existing approaches, and future work of learning to rank. Several learning to rank methods using SVM techniques are described in details.
ER -
English
