Analysis of User Behavior in a Large-Scale VoD System

Understanding streaming user behavior is crucial to the design of large-scale video-on-demand (VoD) systems. However, existing studies usually treat all the users as an entire entity to analyze the collective user behavior. In this paper, we measure the individual viewing behavior of 10 million sampled users from two perspectives: the temporal characteristics and the user interest, and present our results by dividing users into the active and inactive groups. We observe that the active users spend more hours on each active day, and their daily request time distribution is more scattered than that of the inactive users, while the inter-viewing time distribution differs negligible between two groups. We exhibit the similar viewing behaviors of the active and inactive users, e.g. the common interests in popular videos and the latest uploaded videos. We further propose a modified Weibull distribution to fit users' view completion rate, which can deal with different video categories well. To identify users with similar viewing behaviors, we cluster them into 24 classes using their daily request timestamp or 11 classes using the watched video category. The analysis of cluster centroid manifests the efficacy of the clustering, which enables us to step closer to the understanding of user behavior in large-scale VoD systems.