RV-SVM: An efficient method for learning ranking SVM

Yu Hwanjo; Kim Youngdae; Hwang Seungwon

doi:10.1007/978-3-642-01307-2_39

RV-SVM: An efficient method for learning ranking SVM

Yu Hwanjo, Kim Youngdae, Hwang Seungwon

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

Learning ranking (or preference) functions has become an important data mining task in recent years, as various applications have been found in information retrieval. Among rank learning methods, ranking SVMhas been favorably applied to various applications, e.g., optimizing search engines, improving data retrieval quality. In this paper, we first develop a 1-norm ranking SVM that is faster in testing than the standard ranking SVM, and propose Ranking Vector SVM (RV-SVM) that revises the 1-norm ranking SVM for faster training. The number of variables in the RV-SVM is significantly smaller, thus the RV-SVM trains much faster than the other ranking SVMs.We experimentally compared the RV-SVM with the state-of-the-art rank learning method provided in SVM-light. The RV-SVMuses much less support vectors and trains much faster for nonlinear kernels than the SVM-light. The accuracies of RV-SVM and SVM-light are comparable on relatively large data sets. Our implementation of RV-SVM is posted at http://iis.postech.ac.kr/rv-svm.

Original language	English
Title of host publication	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages	426-438
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-642-01307-2_39
Publication status	Published - 2009
Event	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand Duration: 2009 Apr 27 → 2009 Apr 30

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5476 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/Territory	Thailand
City	Bangkok
Period	09/4/27 → 09/4/30

Bibliographical note

Funding Information:
This work was supported by the Korea Research Foundation Grant funded by the Korean Government (KRF-2008-331-D00528).

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-01307-2_39

Cite this

Hwanjo, Y., Youngdae, K., & Seungwon, H. (2009). RV-SVM: An efficient method for learning ranking SVM. In 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 (pp. 426-438). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI). https://doi.org/10.1007/978-3-642-01307-2_39

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abstract = "Learning ranking (or preference) functions has become an important data mining task in recent years, as various applications have been found in information retrieval. Among rank learning methods, ranking SVMhas been favorably applied to various applications, e.g., optimizing search engines, improving data retrieval quality. In this paper, we first develop a 1-norm ranking SVM that is faster in testing than the standard ranking SVM, and propose Ranking Vector SVM (RV-SVM) that revises the 1-norm ranking SVM for faster training. The number of variables in the RV-SVM is significantly smaller, thus the RV-SVM trains much faster than the other ranking SVMs.We experimentally compared the RV-SVM with the state-of-the-art rank learning method provided in SVM-light. The RV-SVMuses much less support vectors and trains much faster for nonlinear kernels than the SVM-light. The accuracies of RV-SVM and SVM-light are comparable on relatively large data sets. Our implementation of RV-SVM is posted at http://iis.postech.ac.kr/rv-svm.",

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Hwanjo, Y, Youngdae, K & Seungwon, H 2009, RV-SVM: An efficient method for learning ranking SVM. in 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5476 LNAI, pp. 426-438, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, Bangkok, Thailand, 09/4/27. https://doi.org/10.1007/978-3-642-01307-2_39

RV-SVM : An efficient method for learning ranking SVM. / Hwanjo, Yu; Youngdae, Kim; Seungwon, Hwang.

13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 426-438 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Learning ranking (or preference) functions has become an important data mining task in recent years, as various applications have been found in information retrieval. Among rank learning methods, ranking SVMhas been favorably applied to various applications, e.g., optimizing search engines, improving data retrieval quality. In this paper, we first develop a 1-norm ranking SVM that is faster in testing than the standard ranking SVM, and propose Ranking Vector SVM (RV-SVM) that revises the 1-norm ranking SVM for faster training. The number of variables in the RV-SVM is significantly smaller, thus the RV-SVM trains much faster than the other ranking SVMs.We experimentally compared the RV-SVM with the state-of-the-art rank learning method provided in SVM-light. The RV-SVMuses much less support vectors and trains much faster for nonlinear kernels than the SVM-light. The accuracies of RV-SVM and SVM-light are comparable on relatively large data sets. Our implementation of RV-SVM is posted at http://iis.postech.ac.kr/rv-svm.

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Hwanjo Y, Youngdae K, Seungwon H. RV-SVM: An efficient method for learning ranking SVM. In 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 426-438. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-01307-2_39

RV-SVM: An efficient method for learning ranking SVM

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