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| 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 |
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| 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 | |
| Copyright and reproduction |
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 |
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| 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 |
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| 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 |
| #Pages | 7 |
| Date of Issue | 2011-03-21 (IBISML) |
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