Early Predictions of Movie Success: The Who, What, and When of Profitability

• 成果类型: Article
• 署名作者: Lash, Michael T.; Zhao, Kang
• 署名单位: University of Iowa; University of Iowa
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2016.1243969
• 发表日期: 2016
• 页码: 874-903
• 关键词: box-office; Network structure; INFORMATION; recommendations; collaboration; SYSTEM

摘要

We focus on predicting the profitability of a movie to support movie-investment decisions at early stages of film production. By leveraging data from various sources, and using social network analysis and text mining techniques, the proposed system extracts several types of features, including who is in the cast, what a movie is about, when a movie will be released, as well as hybrid features. Experiment results showed that the system outperforms benchmark methods by a large margin. Novel features we proposed made weighty contributions to the prediction. In addition to designing a decision support system with practical utility, we also analyzed key factors of movie profitability. Furthermore, we demonstrated the prescriptive value of our system by illustrating how it can be used to recommend a set of profit-maximizing cast members. This research highlights the power of predictive and prescriptive data analytics in information systems to aid business decisions.