A Gaussian copula regression model for movie box-office revenues prediction

Abstract

In this article, we revisit the task of movie box-office revenues prediction using multi-type features. The movie box-office revenues are affected by numerous factors. Previous work with discriminative models assumes these factors are identically and independently distributed. The correlations between these factors are rarely considered, which limited the performances of discriminative models in this task. To address these problems, we investigate a novel Gaussian copula regression model. Based on this model, we do not need to make any prior assumptions about the marginal distributions of the features. In particular, we perform a cumulative probability estimation on each of the smoothed features. The estimation learns the marginal distributions and maps all features into a uniform vector space. Sequentially, we bridge the marginal distributions with a copula function to create their joint distribution, and learn the dependency structure between them. Moreover, we propose a computational-efficient approximate algorithm for responsible variable inference. Experimental results on two movie datasets from Chinese and U.S. market show that our approach outperforms strong discriminative regression baselines.

摘要

本文中, 我们讨论利用多种特征进行电影票房预测的任务。影响电影票房的因素有很多。之前的工作采用的判别模型假设影响电影票房的这些因素是独立同分布的。这些因素之间的关联性很少被考虑, 这样的假设限制了判别模型在此任务上的效果。为了处理这些问题, 我们采用了一个全新的高斯连接回归模型。基于此模型, 我们不需要对特征的边缘分布作任何先验假设。特别地, 我们首先对平滑处理后的特征进行累积概率分布进行估计。通过估计我们学习到了特征的边缘分布, 同时将特征投影到同一向量空间。随后, 我们通过高斯连接函数将这些边缘分布转化为它们的联合分布, 同时获得这些边缘分布之间的依赖关系。此外, 我们还针对联合分布提出了一种高效的因变量推断的近似算法。在两个来自美国和中国电影市场的数据集上的实验结果证明我们的方法表现优于判别模型基线方法。