Modeling Dynamics of Online Video Popularity
This work addresses the need for better video popularity prediction to improve service quality and operating efficiency for large Internet video delivery systems, representing an incremental advance over previous simple models.
The authors tackled the problem of predicting online video popularity by developing a stochastic fluid model that captures hidden processes like information spreading and user reaction, and validated it by matching predictions with observed patterns from a large content provider in China.
Large Internet video delivery systems serve millions of videos to tens of millions of users on daily basis, via Video-on-Demand and live streaming. Video popularity evolves over time. It represents the workload, as welll 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 models for video popularity, or directly adopted patterns from measurement studies. In this paper, we develop a stochastic fluid model that tries to capture two hidden processes that give rise to different patterns of a given video's popularity evolution: the information spreading process, and the user reaction process. Specifically, these processes model how the video is recommended to the user, the videos inherent attractiveness, and users reaction rate, and yield specific popularity evolution patterns. We then validate our model by matching the predictions of the model with observed patterns from our collaborator, a large content provider in China. This model thus gives us the insight to explain the common and different video popularity evolution patterns and why.