A SURVEY ON LEARNING TO RANK

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.

关键词： Ranking learning to rank information retrieval support vector machine ordinal regression evaluation

作者: CHUAN HE CONG WANG YI-XIN ZHONG RUI-FAN LI

作者单位: School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1734-1739

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）