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 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) |
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MVE2022-14 |
Conference Information |
Committee |
MVE |
Conference Date |
2022-09-08 - 2022-09-09 |
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(See Japanese page) |
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Registration To |
MVE |
Conference Code |
2022-09-MVE |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
Title (in English) |
Self-Supervised Learning for Echo Chamber-aware Friend Recommendation |
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Recommendation |
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Echo chamber |
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Self-supervised Learning |
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1st Author's Name |
Luwei Zhang |
1st Author's Affiliation |
The University of Tokyo (UTokyo) |
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Toshihiko Yamsaski |
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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 |
4 |
Date of Issue |
2022-09-01 (MVE) |
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