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Paper Abstract and Keywords
Presentation 2022-09-09 10:30
Self-Supervised Learning for Echo Chamber-aware Friend Recommendation
Luwei Zhang, Toshihiko Yamsaski (UTokyo) MVE2022-14
Abstract (in Japanese) (See Japanese page) 
(in English) In recommender systems, the creation of echo chambers and filter bubbles obviously lowers the diversity of recommendation contents, and potential new social interactions with users are exposed. To some extent, the problems come from the basic concepts of recommender systems. Recommender systems might lead the users to content that they already like or interact with, which is not what we expect to see. Therefore, a significant challenge in this context is how to avoid the effect of the echo chamber and efficiently learn the representations of users based on their shared content and interaction history. To address the problem, we introduce self-supervised learning into Graph Neural Networks (GNNs) approach to learn the echo chamber-aware user representations. Evaluations over Twitter data showed that the introduction of self-supervised learning helped enhance the performance mainly in terms of precision and relevance.
Keyword (in Japanese) (See Japanese page) 
(in English) Recommendation / Echo chamber / Self-supervised Learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 175, MVE2022-14, pp. 22-25, Sept. 2022.
Paper # MVE2022-14 
Date of Issue 2022-09-01 (MVE) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee MVE  
Conference Date 2022-09-08 - 2022-09-09 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To MVE 
Conference Code 2022-09-MVE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Self-Supervised Learning for Echo Chamber-aware Friend Recommendation 
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Keyword(1) Recommendation  
Keyword(2) Echo chamber  
Keyword(3) Self-supervised Learning  
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1st Author's Name Luwei Zhang  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Toshihiko Yamsaski  
2nd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2022-09-09 10:30:00 
Presentation Time 30 minutes 
Registration for MVE 
Paper # MVE2022-14 
Volume (vol) vol.122 
Number (no) no.175 
Page pp.22-25 
#Pages
Date of Issue 2022-09-01 (MVE) 


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