A Gaussian Copula Regression Model for Movie Box-office Revenue Prediction with Social Media

Abstract

Previous work explored many kinds of features for the task of movie box-office prediction. However, little prior work has investigated the dependency relationships among these features. In this paper, we propose a novel Gaussian Copula regression model to study the correlation among predictive features. In particular, we first extract structured movie metadata and user activities on social media as features. We then apply Gaussian kernel to smooth out the data and learn the covariance matrix among the marginal distributions by maximum likelihood. We propose to approximately infer the movie box-office revenue by exploiting the covariance matrix. Experimental results show that our proposed method outperforms the baseline methods in the first week revenue prediction task and can achieve comparable performance on the gross revenue prediction task with a state-of-the art baseline in gross revenue prediction task. Our model is robust under various experimental settings.