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Additional file 4. Arm-specific Checklists for Coaches, Participants, and Scientific Advisors.

Additional file 3. Schedule and Procedures for the Mock Study Section.

Additional file 2. Excerpts from the Regular Dose Coaching Manual.

Additional file 1. Agenda for Kickoff Session to Regular Dose Coaching Intervention.

Additional file 5. Study Assessments and Data Acquisition Schedule.

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., Comment: 6 pages, 3 figures

Video popularity (measured by view count) over time is an essential reference for both online video providers and users. According to state-of-the-art works, video popularity is useful for system optimization, load generation, video caching, and video recommendation. Thus, deeper understanding of video popularity evolution is very helpful for improving video service quality and providers' operating efficiency. The core question to be explored in this paper is what key factors govern online video popularity evolution? Through collaboration with our industry partner, Tencent Video, we obtain historical data of video view counts over a period of time, and observe their patterns. We then propose a stochastic fluid model, named as EvoModel, which captures two processes giving rise to different evolution patterns of a given video: (a) the information spreading process and (b) the user reaction process. The driving forces for process (a) can be either via recommendation from the system directly, or word-of-mouth; the extent of the spread is governed by the intrinsic popularity of the video. The factor affecting the second process can be modeled by a user reaction rate. These processes together determine different video popularity evolution patterns. We validate our model by fitting the historical data obtained from a real-world system. Furthermore, we discuss the feasibility of estimating model parameters and predicting popularity. Refereed/Peer-reviewed