Abstract
Large Internet video delivery systems serve millions of videos to tens of millions of users on daily basis, via Video-on-Demand (VoD) and live streaming. Video popularity (measured by view count) evolves over time. It represents the workload, as well as business value, of the video to the overall system. The ability to predict video popularity is very helpful for improving service quality and operating efficiency. Previous studies adopted simple (usually static) models for video popularity, or directly adopted patterns from measurement studies. In this paper, we develop a fluid model that tries to capture two hidden processes that give rise to different patterns of a given video's popularity evolution: (a) the information spreading process, and (b) the user reaction process. Specifically, these processes model how the video is recommended to the users, the video's inherent attractiveness, and users' reaction rate; and yield different popularity evolution patterns. We validate our model by fitting the data obtained from a large content provider in China. This model gives us the insight to explain the common and different video popularity evolution patterns and why.
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