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| Presentation | 2023-01-22 12:35 Comparing Neural Network-Driven Algorithms that Determine Interest Similarity for People-to-people Recommendations Ana Quelhas (KIT), Natsuki Oka (MSU), Kazuaki Tanaka (KIT)  HCS2022-67 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | With evolving globalization and interpersonal relationships being forged over digital mediums more and more often, the need for reliable people-to-people recommendations increases. The present study aims to evaluate a matchmaking system for friendship recommendation, with the purpose of achieving a reliable result on low quantities of data. Participants were asked to provide information about themselves and their interests, and rate other participants positively or negatively on whether or not they were interested in seeking a friendship with them, based on a summary of their interests and personal information. Their interests were represented as word embeddings and their similarity was determined by various machine learning algorithms, e.g., neural networks. This paper investigated the validity of those algorithms for friendship recommendation. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Word embedding / Matchmaking / Neural networks / Similarity / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 349, HCS2022-67, pp. 71-76, Jan. 2023. | |
| Paper # | HCS2022-67 | |
| Date of Issue | 2023-01-14 (HCS) | |
| 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 | HCS2022-67 |
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| Conference Information | |
| Committee | HCS |
| Conference Date | 2023-01-21 - 2023-01-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyoto Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Physical Communication, etc. |
| Paper Information | |
| Registration To | HCS |
| Conference Code | 2023-01-HCS |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Comparing Neural Network-Driven Algorithms that Determine Interest Similarity for People-to-people Recommendations |
| Sub Title (in English) | |
| Keyword(1) | Word embedding |
| Keyword(2) | Matchmaking |
| Keyword(3) | Neural networks |
| Keyword(4) | Similarity |
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| 1st Author's Name | Ana Quelhas |
| 1st Author's Affiliation | Kyoto Institute of Technology (KIT) |
| 2nd Author's Name | Natsuki Oka |
| 2nd Author's Affiliation | Miyazaki Sangyo-Keiei University (MSU) |
| 3rd Author's Name | Kazuaki Tanaka |
| 3rd Author's Affiliation | Kyoto Institute of Technology (KIT) |
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| Speaker | Author-1 |
| Date Time | 2023-01-22 12:35:00 |
| Presentation Time | 25 minutes |
| Registration for | HCS |
| Paper # | HCS2022-67 |
| Volume (vol) | vol.122 |
| Number (no) | no.349 |
| Page | pp.71-76 |
| #Pages | 6 |
| Date of Issue | 2023-01-14 (HCS) |
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