@inproceedings{1f5d4339ec2b4dddbb1683e3591bd081,
title = "OrdRank: Learning to rank with ordered multiple hyperplanes",
abstract = "Ranking is a central problem for information retrieval systems, because the performance of an information retrieval system is mainly evaluated by the effectiveness of its ranking results. Learning to rank has received much attention in recent years due to its importance in information retrieval. This paper focuses on learning to rank in document retrieval and presents a ranking model named OrdRank that ranks documents with ordered multiple hyperplanes. Comparison of OrdRank with other state-of-the-art ranking techniques is conducted and several evaluation criteria are employed to evaluate its performance. Experimental results on the OHSUMED dataset show that OrdRank outperforms other methods, both in terms of quality of ranking results and efficiency.",
keywords = "Learning to rank, Multiple hyperplanes, Order",
author = "Heli Sun and Jianbin Huang and Boqin Feng and Tao Li and Yingliang Zhao and Jun Liu",
year = "2009",
doi = "10.1109/WI-IAT.2009.93",
language = "英语",
isbn = "9780769538013",
series = "Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009",
pages = "560--563",
booktitle = "Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009",
note = "2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009 ; Conference date: 15-09-2009 Through 18-09-2009",
}