Current state-of-the-art Online Video Services (OVSs) need to simultaneously serve hundreds of millions of users at peak time. To improve service efficiency, the caching strategies of their Content Delivery Networks (CDNs) usually work with video segments of a couple of seconds in length, which poses great challenges in popular prediction of video segments. Popularity prediction of entire videos is generally based on the correlation of early views and future views. However, for the popularity prediction of video segments, the first and the rest segments of a video should be distinguished first because users usually start watching a video from its first segment, but could skip any of the rest ones. Towards this end, we propose a novel method for video segment popularity prediction, in which, the popularity of videos' first and rest segments are predicted with different models. For the first segments, the popularity is predicted like entire videos by using a Multi-Linear Regression (MLR) Model based on past popularity. And for the rest segments, as the jumping viewing behavior has largely weaken the indicating power of their early views on future views, we predict their popularity based on the recent viewed previous segments. Specially, considering the real-time requirement of CDNs caching, it is infeasible to investigate the viewed segments sequences of users with sequence models, hence the prediction model is built on statistic level. Moreover, to maintain the performance in online prediction, we equip the two prediction models with online learning capability so as to timely capture the changes in popularity. For promptly service, the job is fulfilled with an online compensator instead of retraining the two prediction models with revealed target of past predictions. The online compensator estimates the prediction errors of the two models through a LSTM network based on their recent errors and the popularity data corresponded to recent tasks. Through extensive experiments on real-world data of iQIYI, one of the most popular OVS providers in China, we have demonstrated the promise of the proposed method.

Predicting the top-N popular videos and their future views for a large batch of newly uploaded videos is of great commercial value to online video services (OVSs). Although many attempts have been made on video popularity prediction, the existing models has a much lower performance in predicting the top-N popular videos than that of the entire video set. The reason for this phenomenon is that most videos in an OVS system are unpopular, so models preferentially learn the popularity trends of unpopular videos to improve their performance on the entire video set. However, in most cases, it is critical to predict the performance on the top-N popular videos, which is the focus of this study. The challenge for the task are as follows. First, popular and unpopular videos may have similar early view patterns. Second, prediction models that are overly dependent on early view patterns limit the effects of other features. To address these challenges, we propose a novel multifactor differential influence prediction model based on multivariate linear regression. The model is designed to improve the discovery of popular videos and their popularity trends are learnt by enhancing the discriminative power of early patterns for different popularity trends and by optimizing the utilization of multisource data. We evaluate the proposed model using real-world YouTube data, and extensive experiments have demonstrated the effectiveness of our model.