Evolving Deep Neural Networks for Movie Box-office Revenues Prediction

Reliable prediction of movie box-office revenues can greatly reduce the financial risk in the film industry, but accurate prediction is not easy to obtain. Recently, deep neural networks has been applied on movie box-office revenues prediction problems as a promising solution. However, the architecture has a significant impact on its performance, and generally involves a heavy burden of manually designing which is unable to traverse the space of possible architectures efficiently. As a result, the applicability and performance of deep neural networks are severely limited. This paper proposes a new evolutionary algorithm for evolving deep neural networks for movie box-office revenues prediction. In particular, a deep neural network that fuses features extracted from movie posters by a convolutional neural network is introduced first, then a set of novel genetic operators are designed correspondingly. The proposed method can automate the deep neural network architecture designing and aim to search the optimal architecture for movie box-office revenues prediction. Experiments carried out on the Internet Movie Database (IMDB) dataset show that the proposed algorithm achieves superior performance compare to other competitive approaches.