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Paper Abstract and Keywords
Presentation 2012-06-19 16:00
Topic Extraction Method by Semi-Supervised Latent Dirichlet Allocation for Real-Time Recommendation
Yasuhiro Ikeda, Ryoichi Kawahara, Hiroshi Saito (NTT) IBISML2012-6
Abstract (in Japanese) (See Japanese page) 
(in English) Probabilistic topic models for unsupervised learning have been attracting attention
as technology of analyzing user's interest from web activity data of the user.
Especially, Latent Dirichlet Allocation (LDA) is a representative probabilistic topic model.
However, it requires much time to analyze user's web activity data by using LDA when the number of target users is large,
thus there is a possibility that real-time recommendation such as a news recommendation cannot be achieved.
In this paper, we propose a semi-supervised LDA model with Markov property in terms of user's topics,
where we give labels to the user activity on websites by using external information,
and we assume that there is high similarity between two topics of documents the user browsed consecutively.
Through simulation analysis, we show that the proposed model requires only one-tenth as many iterations
to reduce test-set perplexity at the same level as normal LDA.
Moreover, we show that we can obtain intended topics by introducing labeled data.
Keyword (in Japanese) (See Japanese page) 
(in English) recommendation / LDA / semi-supervised learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 83, IBISML2012-6, pp. 35-40, June 2012.
Paper # IBISML2012-6 
Date of Issue 2012-06-12 (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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF IBISML2012-6

Conference Information
Committee IBISML  
Conference Date 2012-06-19 - 2012-06-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Campus plaza Kyoto 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General topics on machine learning and its application 
Paper Information
Registration To IBISML 
Conference Code 2012-06-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Topic Extraction Method by Semi-Supervised Latent Dirichlet Allocation for Real-Time Recommendation 
Sub Title (in English)  
Keyword(1) recommendation  
Keyword(2) LDA  
Keyword(3) semi-supervised learning  
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1st Author's Name Yasuhiro Ikeda  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Ryoichi Kawahara  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Hiroshi Saito  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2012-06-19 16:00:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # IBISML2012-6 
Volume (vol) vol.112 
Number (no) no.83 
Page pp.35-40 
#Pages 6 
Date of Issue 2012-06-12 (IBISML) 

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