User Behavior Analysis and Video Popularity Prediction on a Large-Scale VoD System

Abstract

Understanding streaming user behavior is crucial to the design of large-scale Video-on-Demand (VoD) systems. In this article, we begin with the measurement of individual viewing behavior from two aspects: the temporal characteristics and user interest. We observe that active users spend more hours on each active day, and their daily request time distribution is more scattered than that of the less active users, while the inter-view time distribution differs negligibly between two groups. The common interest in popular videos and the latest uploaded videos is observed in both groups. We then investigate the predictability of video popularity as a collective user behavior through early views. In the light of the limitations of classical approaches, the Autoregressive-Moving-Average (ARMA) model is employed to forecast the popularity dynamics of individual videos at fine-grained time scales, thus achieving much higher prediction accuracy. When applied to video caching, the ARMA-assisted Least Frequently Used (LFU) algorithm can outperform the Least Recently Used (LRU) by 11--16%, the well-tuned LFU by 6--13%, and the LFU is only 2--4% inferior to the offline LFU in terms of hit ratio.