标题:Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm
作者:He, Qiang ;Ma, Jun ;Wang, Shuaiqiang
通讯作者:He, Q
作者机构:[He, Qiang ;Ma, Jun ] School of Computer Science and Technology, Shandong University, Jinan, China;[Wang, Shuaiqiang ] Department of Computer Science, 更多
会议名称:19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
会议日期:26 October 2010 through 30 October 2010
来源:International Conference on Information and Knowledge Management, Proceedings
出版年:2010
页码:1449-1452
DOI:10.1145/1871437.1871644
关键词:Clonal selection algorithm; Information retrieval; Learning to rank; Machine learning; Ranking function
摘要:One fundamental issue of learning to rank is the choice of loss function to be optimized. Although the evaluation measures used in Information Retrieval (IR) are ideal ones, in many cases they can't be used directly because they do not satisfy the smooth property needed in conventional machine learning algorithms. In this paper a new method named RankCSA is proposed, which tries to use IR evaluation measure directly. It employs the clonal selection algorithm to learn an effective ranking function by combining various evidences in IR. Experimental results on the LETOR benchmark datasets demonstrate that RankCSA outperforms the baseline methods in terms of P@n, MAP and NDCG@n. © 2010 ACM.
收录类别:EI;SCOPUS
Scopus被引频次:3
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651343722&doi=10.1145%2f1871437.1871644&partnerID=40&md5=2caad6588658d96d79e90e61d50095b2
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