DeepFusion: predicting movie popularity via cross-platform feature fusion

For online video service providers, the accurate prediction of video popularity directly impacts their advertisement revenue, bandwidth provisioning policy and copyright procurement decision. Most of previous approaches only utilize data from a single platform (e.g., view history) for prediction. However, such approaches cannot provide satisfactory prediction accuracy, as video popularity may be affected by many influential features dispersed over multiple platforms. In this paper, we focus on the popularity prediction of online movies and propose a prediction framework called DeepFusion to fuse salient features from multiple platforms so as to boost the accuracy of popularity prediction of online movies. For this purpose, we extract influential factors from Douban, which is a leading movie rating website in China, and Youku, which is one of the largest online video service providers in China. Considering the complexity incurred by numerous parameters, we choose to feed these influential factors into deep neural networks for prediction and thus avoid the limitation of traditional predictive models. Compared with previous approaches, our solution can significantly improve the prediction accuracy over 40%. Moreover, even for movies without any historical views, our approach can also well capture their popular trends and overcome the cold-start problem.