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Listwise Learning to Rank by Exploring Structure of Objects
Wu, Ou1; You, Qiang1; Mao, Xue1; Xia, Fen2; Yuan, Fei1; Hu, Weiming1
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
2016-07-01
Volume28Issue:7Pages:1934-1939
SubtypeArticle
Abstract11111111; 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.
KeywordListwise Learning To Rank Clustering Multi-task Learning Structure
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TKDE.2016.2535214
Indexed BySCI
Language英语
Funding OrganizationNSFC(61379098)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000380117500024
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12021
Collection模式识别国家重点实验室_视频内容安全
Corresponding AuthorWu, Ou
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
2.Baidu Inc, Big Data Lab, Beijing, Peoples R China
Recommended Citation
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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