Abstract
The movie is one of the integral components of our everyday entertainment. The worldwide movie industry is one of the most growing and significant industries and seizing the attention of people of all ages. It has been observed in the recent study that only a few of the movies achieve success. Uncertainty in the sector has created immense pressure on the film production stakeholder. Moviemakers and researchers continuously feel it necessary to have some expert systems predicting the movie success probability preceding its production with reasonable accuracy. A maximum of the research work has been conducted to predict the movie popularity in the post-production stage. To help the movie maker estimate the upcoming film and make necessary changes, we need to conduct the prediction at the early stage of movie production and provide specific observations about the upcoming movie. This study has proposed a content-based (CB) movie recommendation system (RS) using preliminary movie features like genre, cast, director, keywords, and movie description. Using RS output and movie rating and voting information of similar movies, we created a new feature set and proposed a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. We also proposed a system to predict the popularity of the upcoming movie among different audience groups. We have divided the audience group into four age groups junior, teenage, mid-age and senior. This study has used publicly available Internet Movie Database (IMDb) data and The Movie Database (TMDb) data. We had implemented a multiclass classification model and achieved 96.8% accuracy, which outperforms all the benchmark models. This study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decisions.