  Video popularity is an essential reference for optimizing resource allocation
and video recommendation in online video services. However, there is still no
convincing model that can accurately depict a video's popularity evolution. In
this paper, we propose a dynamic popularity model by modeling the video
information diffusion process driven by various forms of recommendation.
Through fitting the model with real traces collected from a practical system,
we can quantify the strengths of the recommendation forces. Such quantification
can lead to characterizing video popularity patterns, user behaviors and
recommendation strategies, which is illustrated by a case study of TV episodes.
