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Paper Abstract and Keywords
Presentation 2020-05-29 13:55
An Efficient Recommendation System Based on Spectral Analysis of Review Data
Koki Tozuka, Goutam Chakraborty, Masafumi Matsuhara, Hiroshi Mabuchi (Iwate Prefectural Univ) SC2020-2
Abstract (in Japanese) (See Japanese page) 
(in English) The purpose of this research is to improve the accuracy of recommendation systems for real-world review data. With increasing popularity of e-commerce, the scale of review data in the real world is enormous, with thousands of items and millions of users. As the review data matrix is extremely sparse, smoothing it to have a sufficiently accurate recommendation system is difficult, using conventional methods of clustering as a model of collaborative filtering. An efficient and accurate tool to have sufficient accuracy is required. In this research, we propose a clustering method for recommendation system that uses matrix spectral clustering, focusing to overcome the problem of large sparseness of review data and find subtle relationship between items. From the experimental results, the proposed method could achieve the highest recommendation accuracy compared to the cluster models
based on K-means++, and agglomerative hierarchical clustering.
Keyword (in Japanese) (See Japanese page) 
(in English) Recommendation System / Spectral Analysis / Clustering / Laplacian Matrix / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 49, SC2020-2, pp. 7-11, May 2020.
Paper # SC2020-2 
Date of Issue 2020-05-22 (SC) 
ISSN 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 SC2020-2

Conference Information
Committee SC  
Conference Date 2020-05-29 - 2020-05-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) AI Application for Service Computing Environment and Other Issues 
Paper Information
Registration To SC 
Conference Code 2020-05-SC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Efficient Recommendation System Based on Spectral Analysis of Review Data 
Sub Title (in English)  
Keyword(1) Recommendation System  
Keyword(2) Spectral Analysis  
Keyword(3) Clustering  
Keyword(4) Laplacian Matrix  
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Keyword(6)  
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1st Author's Name Koki Tozuka  
1st Author's Affiliation Iwate Prefectural University Graduate School (Iwate Prefectural Univ)
2nd Author's Name Goutam Chakraborty  
2nd Author's Affiliation Iwate Prefectural University (Iwate Prefectural Univ)
3rd Author's Name Masafumi Matsuhara  
3rd Author's Affiliation Iwate Prefectural University (Iwate Prefectural Univ)
4th Author's Name Hiroshi Mabuchi  
4th Author's Affiliation Iwate Prefectural University (Iwate Prefectural Univ)
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Speaker Author-1 
Date Time 2020-05-29 13:55:00 
Presentation Time 25 minutes 
Registration for SC 
Paper # SC2020-2 
Volume (vol) vol.120 
Number (no) no.49 
Page pp.7-11 
#Pages 5 
Date of Issue 2020-05-22 (SC) 

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