Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/633030
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dc.contributor.authorZhiyi Tanen_US
dc.contributor.authorYanfeng Wangen_US
dc.contributor.authorYa Zhangen_US
dc.contributor.authorJun Zhouen_US
dc.date.accessioned2020-05-20T09:52:59Z-
dc.date.available2020-05-20T09:52:59Z-
dc.date.issued2016en_US
dc.identifier.issn0018-9316en_US
dc.identifier.issn1557-9611en_US
dc.identifier.other10.1109/TBC.2016.2540522en_US
dc.identifier.urihttp://localhost/handle/Hannan/166554en_US
dc.identifier.urihttp://localhost/handle/Hannan/633030-
dc.description.abstractPredicting the video popularity is an essential part of fast growing online media services. It is beneficial to an array of domains, from targeted advertising, personalized recommendation, to traffic load optimization. However, popularity prediction is a challenge problem due to the uncertainty of information cascade. In this paper, we treat the popularity of online videos as time series over the given periods and propose a novel time series model for popularity prediction. The proposed model is based on the correlation between early and future popularity series. Instead of inferring the precise view counts for a video, this paper focuses on accurately identifying the most popular videos based on the predicted popularity, because it is of the most interest to service providers. Experimental result on real world data have demonstrated that the proposed model outperforms several existing popularity prediction models.en_US
dc.publisherIEEEen_US
dc.relation.haspart7454717.pdfen_US
dc.subjecttime series analysis|Popularity prediction|accumulative view counts index (AVCI)|view counts dynamic model (VCDM)en_US
dc.titleA Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videosen_US
dc.typeArticleen_US
dc.journal.volume62en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Broadcastingen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhiyi Tanen_US
dc.contributor.authorYanfeng Wangen_US
dc.contributor.authorYa Zhangen_US
dc.contributor.authorJun Zhouen_US
dc.date.accessioned2020-05-20T09:52:59Z-
dc.date.available2020-05-20T09:52:59Z-
dc.date.issued2016en_US
dc.identifier.issn0018-9316en_US
dc.identifier.issn1557-9611en_US
dc.identifier.other10.1109/TBC.2016.2540522en_US
dc.identifier.urihttp://localhost/handle/Hannan/166554en_US
dc.identifier.urihttp://localhost/handle/Hannan/633030-
dc.description.abstractPredicting the video popularity is an essential part of fast growing online media services. It is beneficial to an array of domains, from targeted advertising, personalized recommendation, to traffic load optimization. However, popularity prediction is a challenge problem due to the uncertainty of information cascade. In this paper, we treat the popularity of online videos as time series over the given periods and propose a novel time series model for popularity prediction. The proposed model is based on the correlation between early and future popularity series. Instead of inferring the precise view counts for a video, this paper focuses on accurately identifying the most popular videos based on the predicted popularity, because it is of the most interest to service providers. Experimental result on real world data have demonstrated that the proposed model outperforms several existing popularity prediction models.en_US
dc.publisherIEEEen_US
dc.relation.haspart7454717.pdfen_US
dc.subjecttime series analysis|Popularity prediction|accumulative view counts index (AVCI)|view counts dynamic model (VCDM)en_US
dc.titleA Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videosen_US
dc.typeArticleen_US
dc.journal.volume62en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Broadcastingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7454717.pdf1.36 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhiyi Tanen_US
dc.contributor.authorYanfeng Wangen_US
dc.contributor.authorYa Zhangen_US
dc.contributor.authorJun Zhouen_US
dc.date.accessioned2020-05-20T09:52:59Z-
dc.date.available2020-05-20T09:52:59Z-
dc.date.issued2016en_US
dc.identifier.issn0018-9316en_US
dc.identifier.issn1557-9611en_US
dc.identifier.other10.1109/TBC.2016.2540522en_US
dc.identifier.urihttp://localhost/handle/Hannan/166554en_US
dc.identifier.urihttp://localhost/handle/Hannan/633030-
dc.description.abstractPredicting the video popularity is an essential part of fast growing online media services. It is beneficial to an array of domains, from targeted advertising, personalized recommendation, to traffic load optimization. However, popularity prediction is a challenge problem due to the uncertainty of information cascade. In this paper, we treat the popularity of online videos as time series over the given periods and propose a novel time series model for popularity prediction. The proposed model is based on the correlation between early and future popularity series. Instead of inferring the precise view counts for a video, this paper focuses on accurately identifying the most popular videos based on the predicted popularity, because it is of the most interest to service providers. Experimental result on real world data have demonstrated that the proposed model outperforms several existing popularity prediction models.en_US
dc.publisherIEEEen_US
dc.relation.haspart7454717.pdfen_US
dc.subjecttime series analysis|Popularity prediction|accumulative view counts index (AVCI)|view counts dynamic model (VCDM)en_US
dc.titleA Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videosen_US
dc.typeArticleen_US
dc.journal.volume62en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Broadcastingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7454717.pdf1.36 MBAdobe PDFThumbnail
Preview File