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| Presentation | 2017-03-06 10:00 Prediction of the Number of Content Requests by Deep Learning Tatsuya Suda (Waseda Univ.), Kyoko Yamori (Asahi Univ./Waseda Univ.), Yoshiaki Tanaka (Waseda Univ.)  CQ2016-111 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In content distribution systems such as CDN (Content Delivery Network), the contents with high request rate should be stored in the edge server for efficient distribution. If the request rate of the new content can be predicted, the network can be used more efficiently. In this paper, the number of content requests is predicted by Deep Learning so as to assign the content to the suitable server. The input parameters of deep learning are title and tag information which are metadata of content, and they are vectorized by natural language. The relationship between the created vector and the number of content requests is evaluated by cosine similarity. If the similarity of the contents is high, the ranking of the number of requests will be almost the same. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep Learning / Content Request / Prediction / Doc2Vec / LDA / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 497, CQ2016-111, pp. 1-6, March 2017. | |
| Paper # | CQ2016-111 | |
| Date of Issue | 2017-02-27 (CQ) | |
| ISSN | Print edition: ISSN 0913-5685 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 | CQ2016-111 |
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| Conference Information | |
| Committee | MVE IE CQ IMQ |
| Conference Date | 2017-03-06 - 2017-03-07 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyusyu Univ. Ohashi Campus |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Five senses media, Multimedia, Virtual Environment, Image encoding, Ultra realistic, Network quality and reliability, Image media quality, etc. |
| Paper Information | |
| Registration To | CQ |
| Conference Code | 2017-03-MVE-IE-CQ-IMQ |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Prediction of the Number of Content Requests by Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | Content Request |
| Keyword(3) | Prediction |
| Keyword(4) | Doc2Vec |
| Keyword(5) | LDA |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Tatsuya Suda |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Kyoko Yamori |
| 2nd Author's Affiliation | Asahi University/Waseda University (Asahi Univ./Waseda Univ.) |
| 3rd Author's Name | Yoshiaki Tanaka |
| 3rd Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2017-03-06 10:00:00 |
| Presentation Time | 25 minutes |
| Registration for | CQ |
| Paper # | CQ2016-111 |
| Volume (vol) | vol.116 |
| Number (no) | no.497 |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2017-02-27 (CQ) |
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