Paper Abstract and Keywords |
Presentation |
2016-07-16 09:00
Analysis and Prediction of Popularity Dynamics of User Generated Contents Tatsuya Tanaka (Osaka Univ.), Shingo Ata (Osaka City Univ.), Masayuki Murata (Osaka Univ.) IN2016-31 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In recent years, social networking services such as YouTube, which share UGC (User Generated Contents) have become much attracted. An efficient control of UGC is one of important role to achieve an optimized placement of advertisements for end users, and/or content-aware caching control for improve the utilization of network resources. To this end, it is effective to forecast the future popularity of the content as early as possible, so that we can take a proactive action to highly popular contents. In this paper, we propose a method to classify time dependent variations of popularity (popularity patterns) of UGCs by using k-means clustering, and analyze tendencies led by popularity patterns. We then propose a method to identify UGCs which are expected to be popular in future, by taking both the initial part of popularity patterns and actual counts of downloads into consideration. Our experimental results show that the accuracy of identification of popular UGCs can be increased around 10% by considering the initial part of popularity patterns. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
YouTube / popularity prediction / view count transition pattern / k-means / Naive Bayes Classifier / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 137, IN2016-31, pp. 49-54, July 2016. |
Paper # |
IN2016-31 |
Date of Issue |
2016-07-08 (IN) |
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 |
IN2016-31 |
Conference Information |
Committee |
IN NV |
Conference Date |
2016-07-15 - 2016-07-16 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Matsumaecho Sougo Center |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Next Generation/New Generation/Future Network, Cloud/Data Center Network, SDN (Open Flow etc.), NFV, IPv6, Overlay Network, P2P, Content Distribution, Content Exchange, TCP/IP, BGP, DNS, HTTP/2, Routing, Switching, Traffic Engineering, etc. |
Paper Information |
Registration To |
IN |
Conference Code |
2016-07-IN-NV |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Analysis and Prediction of Popularity Dynamics of User Generated Contents |
Sub Title (in English) |
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YouTube |
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popularity prediction |
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view count transition pattern |
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k-means |
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Naive Bayes Classifier |
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1st Author's Name |
Tatsuya Tanaka |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Shingo Ata |
2nd Author's Affiliation |
Osaka City University (Osaka City Univ.) |
3rd Author's Name |
Masayuki Murata |
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Osaka University (Osaka Univ.) |
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Speaker |
Author-1 |
Date Time |
2016-07-16 09:00:00 |
Presentation Time |
25 minutes |
Registration for |
IN |
Paper # |
IN2016-31 |
Volume (vol) |
vol.116 |
Number (no) |
no.137 |
Page |
pp.49-54 |
#Pages |
6 |
Date of Issue |
2016-07-08 (IN) |
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