Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

Abstract

Movies are an important part of our daily entertainment, with the global movie industry experiencing significant growth and capturing the attention of people of all ages. However, only a few movies achieve success, leading to pressure on movie production stakeholders. Therefore, researchers and moviemakers require expert systems that can accurately predict the probability of a movie's success prior to its production. Most research on predicting movie popularity has focused on post-production stages, but it's essential to predict a movie's success at an early stage to enable necessary changes to be made. To this end, a content-based movie recommendation system (RS) has been proposed that uses preliminary movie features such as genre, cast, director, keywords, and movie description. This RS output and movie rating and voting information are then used to create a new feature set, which is input into a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. The study also proposes a system to predict the popularity of the upcoming movie among different age groups, dividing the audience into four categories: junior, teenage, mid-age, and senior. The study uses publicly available data from the Internet Movie Database (IMDb) and The Movie Database (TMDb). The multiclass classification model implemented in this study achieved 96.8% accuracy, outperforming all benchmark models. Overall, this study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decision- making.