Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/633030
Title: A Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videos
Authors: Zhiyi Tan;Yanfeng Wang;Ya Zhang;Jun Zhou
subject: time series analysis|Popularity prediction|accumulative view counts index (AVCI)|view counts dynamic model (VCDM)
Year: 2016
Publisher: IEEE
Abstract: Predicting 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.
URI: http://localhost/handle/Hannan/166554
http://localhost/handle/Hannan/633030
ISSN: 0018-9316
1557-9611
volume: 62
issue: 2
Appears in Collections:2016

Files in This Item:
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7454717.pdf1.36 MBAdobe PDFThumbnail
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Title: A Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videos
Authors: Zhiyi Tan;Yanfeng Wang;Ya Zhang;Jun Zhou
subject: time series analysis|Popularity prediction|accumulative view counts index (AVCI)|view counts dynamic model (VCDM)
Year: 2016
Publisher: IEEE
Abstract: Predicting 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.
URI: http://localhost/handle/Hannan/166554
http://localhost/handle/Hannan/633030
ISSN: 0018-9316
1557-9611
volume: 62
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7454717.pdf1.36 MBAdobe PDFThumbnail
Preview File
Title: A Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videos
Authors: Zhiyi Tan;Yanfeng Wang;Ya Zhang;Jun Zhou
subject: time series analysis|Popularity prediction|accumulative view counts index (AVCI)|view counts dynamic model (VCDM)
Year: 2016
Publisher: IEEE
Abstract: Predicting 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.
URI: http://localhost/handle/Hannan/166554
http://localhost/handle/Hannan/633030
ISSN: 0018-9316
1557-9611
volume: 62
issue: 2
Appears in Collections:2016

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