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Conference Paper: Guiding internet-scale video service deployment using microblog-based prediction

TitleGuiding internet-scale video service deployment using microblog-based prediction
Authors
KeywordsClassical approach
Geographic distribution
Geographic regions
Influential factors
Internet video
Issue Date2012
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
Citation
The 31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012), Orlando, FL., 25-30 March 2012. In IEEE Infocom Proceedings, 2012, p. 2901-2905 How to Cite?
AbstractOnline microblogging has been very popular in today's Internet, where users exchange short messages and follow various contents shared by people that they are interested in. Among the variety of exchanges, video links are a representative type on a microblogging site. More and more viewers of an Internet video service are coming from microblog recommendations. It is intriguing research to explore the connections between the patterns of microblog exchanges and the popularity of videos, in order to potentially use the propagation patterns of microblogs to guide proactive service deployment of a video sharing system. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing site and a favored microblogging system in China, we explore how patterns of video link propagation in the microblogging system are correlated with video popularity on the video sharing site, at different times and in different geographic regions. Using influential factors summarized from the measurement studies, we further design neural network-based learning frameworks to predict the number of potential viewers of different videos and the geographic distribution of viewers. Experiments show that our neural network-based frameworks achieve better prediction accuracy, as compared to a classical approach that relies on historical numbers of views. We also briefly discuss how proactive video service deployment can be effectively enabled by our prediction frameworks. © 2012 IEEE.
DescriptionMini-Conference - MC16: Social Networks
Persistent Identifierhttp://hdl.handle.net/10722/152048
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorWang, Zen_US
dc.contributor.authorSun, Len_US
dc.contributor.authorWu, Cen_US
dc.contributor.authorYang, Sen_US
dc.date.accessioned2012-06-26T06:32:55Z-
dc.date.available2012-06-26T06:32:55Z-
dc.date.issued2012en_US
dc.identifier.citationThe 31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012), Orlando, FL., 25-30 March 2012. In IEEE Infocom Proceedings, 2012, p. 2901-2905en_US
dc.identifier.isbn978-1-4673-0775-8-
dc.identifier.issn0743-166Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/152048-
dc.descriptionMini-Conference - MC16: Social Networks-
dc.description.abstractOnline microblogging has been very popular in today's Internet, where users exchange short messages and follow various contents shared by people that they are interested in. Among the variety of exchanges, video links are a representative type on a microblogging site. More and more viewers of an Internet video service are coming from microblog recommendations. It is intriguing research to explore the connections between the patterns of microblog exchanges and the popularity of videos, in order to potentially use the propagation patterns of microblogs to guide proactive service deployment of a video sharing system. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing site and a favored microblogging system in China, we explore how patterns of video link propagation in the microblogging system are correlated with video popularity on the video sharing site, at different times and in different geographic regions. Using influential factors summarized from the measurement studies, we further design neural network-based learning frameworks to predict the number of potential viewers of different videos and the geographic distribution of viewers. Experiments show that our neural network-based frameworks achieve better prediction accuracy, as compared to a classical approach that relies on historical numbers of views. We also briefly discuss how proactive video service deployment can be effectively enabled by our prediction frameworks. © 2012 IEEE.en_US
dc.languageengen_US
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359en_US
dc.relation.ispartofIEEE Infocom Proceedingsen_US
dc.rightsIEEE Infocom Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectClassical approach-
dc.subjectGeographic distribution-
dc.subjectGeographic regions-
dc.subjectInfluential factors-
dc.subjectInternet video-
dc.titleGuiding internet-scale video service deployment using microblog-based predictionen_US
dc.typeConference_Paperen_US
dc.identifier.emailWang, Z: wangzhi04@mails.tsinghua.edu.cnen_US
dc.identifier.emailSun, L: sunlf@tsinghua.edu.cn-
dc.identifier.emailWu, C: cwu@cs.hku.hk-
dc.identifier.emailYang, S: yangshq@tsinghua.edu.cn-
dc.identifier.authorityWu, C=rp01397en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1109/INFCOM.2012.6195726en_US
dc.identifier.scopuseid_2-s2.0-84861629704en_US
dc.identifier.hkuros202425-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84861629704&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage2901en_US
dc.identifier.epage2905en_US
dc.publisher.placeUnited Statesen_US
dc.description.otherThe 31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012), Orlando, FL., 25-30 March 2012. In IEEE Infocom Proceedings, 2012, p. 2901-2905-
dc.identifier.scopusauthoridYang, S=7406947569en_US
dc.identifier.scopusauthoridWu, C=15836048100en_US
dc.identifier.scopusauthoridSun, L=7403957453en_US
dc.identifier.scopusauthoridWang, Z=7410041125en_US

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