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Paper Abstract and Keywords
Presentation 2023-09-12 14:55
A Study on Recommending Items with Review Texts by Automatic Scoring of Cosmetics Review Texts
Natsumi Baba, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga (UEC) AI2023-29
Abstract (in Japanese) (See Japanese page) 
(in English) With the spread of e-commerce websites, product review sites have become widely used. The product information available on many of these sites is limited to information such as the composition substance and size of the product presented by the company and the review text freely describing the impressions of those who have actually used the product, making it difficult to grasp the characteristics of the product as felt when using it. In addition, these review sites often include product recommendations, such as "Recommendation for you," but in many cases, only the product is presented, and it is rare to know what the product is good at and why it is recommended for.
In this study, we propose a method to easily present the contents of actual impressions of a product and the reasons for recommendation. Here, we took cosmetics as the subject, and scored the product features on a 5-point scale using reviews actually posted by the users. Based on the results, we used collaborative filtering to determine product recommendations and to create "review sentences that users are expected to write if they used the product. The proposed method aims to improve user satisfaction with product recommendations.
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Reference Info. IEICE Tech. Rep., vol. 123, no. 190, AI2023-29, pp. 167-171, Sept. 2023.
Paper # AI2023-29 
Date of Issue 2023-09-05 (AI) 
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 AI2023-29

Conference Information
Committee AI  
Conference Date 2023-09-12 - 2023-09-13 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To AI 
Conference Code 2023-09-AI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Recommending Items with Review Texts by Automatic Scoring of Cosmetics Review Texts 
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1st Author's Name Natsumi Baba  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Yuichi Sei  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Yasuyuki Tahara  
3rd Author's Affiliation The University of Electro-Communications (UEC)
4th Author's Name Akihiko Ohsuga  
4th Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2023-09-12 14:55:00 
Presentation Time 20 minutes 
Registration for AI 
Paper # AI2023-29 
Volume (vol) vol.123 
Number (no) no.190 
Page pp.167-171 
#Pages 5 
Date of Issue 2023-09-05 (AI) 

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