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摘要
Predicting the popularity of online videos is an important task for the service design, advertisement placement, network management, and so on. In this paper, we tackle the challenge head-on by casting the popularity prediction problem into two consecutive tasks: online video future popularity level prediction and online video future view count prediction. We first predict the future popularity levels of online videos, based on a rich set of features and effective classification technique. Then, according to the popularity level transitions, we build specialized regression models to predict the future view count values. We validate our approach on the exhaustive dataset of a leading online video service provider in China, namely, Youku. The experimental results show that comparing with two state-of-the-art baseline models, our proposed method can significantly decrease the relative prediction errors of 32.25% and 19.82%, respectively. At last, we also discuss the model setup and feature importance of our method. We believe our work can provide direct help in practical for the interested parties of online video service, such as service providers, online advisers, and network operators.
- 出版日期2016
- 单位北京邮电大学
