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

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