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Paper Abstract and Keywords
Presentation 2011-03-28 15:00
A Study on Position-based Adaptive Weighting for Ranking SVM
Masayuki Karasuyama, Takuya Hasegawa, Tsukasa Matsuno, Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2010-115
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a novel training algorithm for ranking support vector machine (ranking SVM). The focus is on how to incorporate position-dependent information in weighted variant of ranking SVM. It has been recognized in information retrieval literature that quality measures (such as normalized discounted cumulative gain: NDCG) for ranking should incorporate position-dependent information because users are only interested in a few top-ranked documents. An important issue in this task is that the final position of each document, i.e., ranking result, is unknown before training the ranking SVM. Our approach in this paper is adaptively changing the weights in the training process based on the position information produced by the ranking SVM currently under training. Specifically, we introduce regularization path-following approach and extend it in such a way that position-dependent weights are adaptively updated by detecting position changes in the regularization path. We present two implementations of this approach: exact one and approximated one. We show that the former can exactly keep track of the position changes, while the latter can be much faster and more scalable than the former by approximating the regularization path. We demonstrate the effectiveness of our approach by applying it to large-scale information retrieval task.
Keyword (in Japanese) (See Japanese page) 
(in English) Learning to rank / Ranking SVM / adaptive weighting / path following / / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 476, IBISML2010-115, pp. 77-83, March 2011.
Paper # IBISML2010-115 
Date of Issue 2011-03-21 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IBISML2010-115

Conference Information
Committee IBISML  
Conference Date 2011-03-28 - 2011-03-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Nakanoshima Center, Osaka Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Text and Web mining, etc. 
Paper Information
Registration To IBISML 
Conference Code 2011-03-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Position-based Adaptive Weighting for Ranking SVM 
Sub Title (in English)  
Keyword(1) Learning to rank  
Keyword(2) Ranking SVM  
Keyword(3) adaptive weighting  
Keyword(4) path following  
1st Author's Name Masayuki Karasuyama  
1st Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
2nd Author's Name Takuya Hasegawa  
2nd Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
3rd Author's Name Tsukasa Matsuno  
3rd Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
4th Author's Name Ichiro Takeuchi  
4th Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
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Speaker Author-1 
Date Time 2011-03-28 15:00:00 
Presentation Time 20 minutes 
Registration for IBISML 
Paper # IBISML2010-115 
Volume (vol) vol.110 
Number (no) no.476 
Page pp.77-83 
Date of Issue 2011-03-21 (IBISML) 

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