2009 First International Conference on Evolving Internet 2009
DOI: 10.1109/internet.2009.22
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Analysis and Modeling of Video Popularity Evolution in Various Online Video Content Systems: Power-Law versus Exponential Decay

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Cited by 45 publications
(34 citation statements)
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“…Additionally, they identified and quantified the main referrers that lead users to videos, as they are key mechanisms in attracting users and thus highly influence popularity evolutions. An alternative approach was proposed by Avramova et al [4]. They found that YouTube video popularity traces follow several different distributions, such as power-law or exponential.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…Additionally, they identified and quantified the main referrers that lead users to videos, as they are key mechanisms in attracting users and thus highly influence popularity evolutions. An alternative approach was proposed by Avramova et al [4]. They found that YouTube video popularity traces follow several different distributions, such as power-law or exponential.…”
Section: Related Workmentioning
confidence: 99%
“…It has been previously proposed in literature as a model for cumulative request patterns of multimedia content [4]. It is characterized by two parameters, C and α, which respectively represent the normalization constant and scaling factor.…”
Section: Predicting Content Popularitymentioning
confidence: 99%
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“…In [2] we proposed an analytical model for the way accumulated views to videos grow. In fact, this analytical model describes the (measured or predicted) accumulated number of views for a video up to a certain moment in time -t.…”
Section: Generic User Behaviour Modelmentioning
confidence: 99%
“…In some studies this volatility of objects has been taken into account by feeding the caching algorithm with a trace obtained from observing requests on an existing web server [6]. In this paper we use the model introduced in [2] that takes the evolution of the video object popularity into account. We tune the parameters of this model based on data we collected for a CUTV service.…”
Section: Introductionmentioning
confidence: 99%