Listwise Learning to Rank by Exploring Structure of Objects

doi:10.1109/TKDE.2016.2535214

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	Listwise Learning to Rank by Exploring Structure of Objects
	Wu, Ou1 ; You, Qiang1 ; Mao, Xue1 ; Xia, Fen 2; Yuan, Fei1 ; Hu, Weiming1
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2016-07-01
卷号	28 期号:7 页码:1934-1939
文章类型	Article
摘要	11111111; Listwise learning to rank (LTR) is aimed at constructing a ranking model from listwise training data to order objects. In most existing studies, each training instance consists of a set of objects described by preference features. In a preference feature space for the objects in training, the structure of the objects is associated with the absolute preference degrees for the objects. The degrees significantly influence the ordering of the objects. Nevertheless, the structure of the training objects in their preference feature space has rarely been studied. In addition, most listwise LTR algorithms yield a single linear ranking model for all objects, but this ranking model may be insufficient to capture the underlying nonlinear ranking mechanism among all objects. This study proposes a divide-and-train method to learn a nonlinear ranking model from listwise training data. First, a rank-preserving clustering approach is used to infer the structure of objects in their preference feature space and all the objects in training data are divided into several clusters. Each cluster is assumed to correspond to a preference degree and an ordinal regression function is then learned. Second, considering that relations exist among the clusters, a multi-task listwise ranking approach is then employed to train linear ranking functions for all the clusters (or preference degrees) simultaneously. Our proposed method utilizes both the (relative) preferences among objects and the intrinsic structure of objects. Experimental results on benchmark data sets suggest that the proposed method outperforms state-ofthe- art listwise LTR algorithms.
关键词	Listwise Learning To Rank Clustering Multi-task Learning Structure
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TKDE.2016.2535214
收录类别	SCI
语种	英语
项目资助者	NSFC(61379098)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000380117500024
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12021
专题	多模态人工智能系统全国重点实验室_视频内容安全
通讯作者	Wu, Ou
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China 2.Baidu Inc, Big Data Lab, Beijing, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wu, Ou,You, Qiang,Mao, Xue,et al. Listwise Learning to Rank by Exploring Structure of Objects[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2016,28(7):1934-1939.
APA	Wu, Ou,You, Qiang,Mao, Xue,Xia, Fen,Yuan, Fei,&Hu, Weiming.(2016).Listwise Learning to Rank by Exploring Structure of Objects.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,28(7),1934-1939.
MLA	Wu, Ou,et al."Listwise Learning to Rank by Exploring Structure of Objects".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 28.7(2016):1934-1939.

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