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Modeling the Parameter Interactions in Ranking SVM with Low-Rank Approximation

文献类型：期刊论文

作者Guo, Jiafeng1; Cheng, Xueqi1; Lan, Yanyan1; Zeng, Wei1; Xu, Jun2
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2019-06-01
卷号31期号:6页码:1181-1193
关键词Learning to rank ranking SVM parameter interactions low-rank approximation
ISSN号1041-4347
DOI10.1109/TKDE.2018.2851257
英文摘要Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of a linear Ranking SVM model can be written as a linear combination of the preference pairs, i.e., w = Sigma((i,j)) alpha(ij) (x(i) - x(j)), where alpha(ij) denotes the Lagrange parameters associated with each preference pair (i, j). It is observed that there exist obvious interactions among the document pairs because two preference pairs could share a same document as their items, e.g., preference pairs (d(1), d(2)) and (d(1), d(3)) share the document d(1). Thus it is natural to ask if there also exist interactions over the model parameters alpha(ij), which may be leveraged to construct better ranking models. This paper aims to answer the question. We empirically found that there exists a low-rank structure over the rearranged Ranking SVM model parameters alpha(ij), which indicates that the interactions do exist. Based on the discovery, we made modifications on the original Ranking SVM model by explicitly applying low-rank constraints to the Lagrange parameters, achieving two novel algorithms called Factorized Ranking SVM and Regularized Ranking SVM, respectively. Specifically, in Factorized Ranking SVM each parameter alpha(ij) is decomposed as a product of two low-dimensional vectors, i.e., alpha(ij) = < v(i), v(j)>, where vectors v(i) and v(j) correspond to document i and j, respectively; In Regularized Ranking SVM, a nuclear norm is applied to the rearranged parameters matrix for controlling its rank. Experimental results on three LETOR datasets show that both of the proposed methods can outperform state-of-the-art learning to rank models including the conventional Ranking SVM.
资助项目National Natural Science Foundation of China (NSFC)[61872338] ; National Natural Science Foundation of China (NSFC)[61773362] ; National Natural Science Foundation of China (NSFC)[61425016] ; National Natural Science Foundation of China (NSFC)[61472401] ; National Natural Science Foundation of China (NSFC)[61722211] ; Youth Innovation Promotion Association CAS[20144310] ; Youth Innovation Promotion Association CAS[2016102]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000466933700012
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/4247]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xu, Jun
作者单位1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China
2.Renmin Univ China, Sch Informat, Beijing Key Lab Big Data Management & Anal Method, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Jiafeng,Cheng, Xueqi,Lan, Yanyan,et al. Modeling the Parameter Interactions in Ranking SVM with Low-Rank Approximation[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2019,31(6):1181-1193.
APA Guo, Jiafeng,Cheng, Xueqi,Lan, Yanyan,Zeng, Wei,&Xu, Jun.(2019).Modeling the Parameter Interactions in Ranking SVM with Low-Rank Approximation.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,31(6),1181-1193.
MLA Guo, Jiafeng,et al."Modeling the Parameter Interactions in Ranking SVM with Low-Rank Approximation".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 31.6(2019):1181-1193.

入库方式： OAI收割

来源：计算技术研究所

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