An Iterative Model for Predicting Film Attendance

  • Yang Yue
  • Ying Li
  • Tong Jia
  • Zhonghai Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)


As an important index during film distribution, film attendance is frequently taken into consideration by distribution companies and theater lines when making decisions about budget allocation. Lacking automatic solutions, film attendance is usually estimated by human expertise, which costs many efforts but still cannot achieve satisfactory accuracy. Therefore, it is important to predict film attendance automatically and accurately during film distribution. In this paper, we propose an approach to predicting film attendance of incoming days with film metadata, audience want data, and attendance pattern. An Attendance Iterative Model (AIM) is constructed by iteratively combining random forest based Base Model and SVM based Auxiliary model. The approach has been evaluated with all films released in China in 2015–2016. The result indicates that our model performs well for various films at most times, which MAE maintains within 2–8. Additionally, our iterative model outperforms multi-model with reasonable accuracy and satisfied flexibility of prediction time range.


Film attendance Machine learning Iterative model 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Software and MicroelectronicsPeking UniversityBeijingChina
  2. 2.National Research Center of Software EngineeringPeking UniversityBeijingChina

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