A survey on learning to rank

被引：12

作者：

He, Chuan ^{[1
]}

Wang, Cong ^{[1
]}

Zhong, Yi-Xin ^{[1
]}

Li, Rui-Fan ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Informat Engn, Beijing 100876, Peoples R China

来源：

PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年

关键词：

ranking; learning to rank; information retrieval; support vector machine; ordinal regression; evaluation;

D O I：

10.1109/ICMLC.2008.4620685

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Ranking is the key problem for information retrieval and other text applications. Recently, the ranking methods based on machine learning approaches, called learning to rank, become the focus for researchers and practitioners. The main idea of these methods is to apply the various existing and effective algorithms on machine learning to ranking. However, as a learning problem, ranking is different from other classical ones such as classification and regression. In this paper, we investigate the important papers in this direction; the cons and pros of the recent-proposed framework and algorithms for ranking are analyzed, and the relationships among them are discussed. Finally, the promising directions in practice are also pointed out.

引用

页码：1734 / 1739

页数：6