An Intelligent Decision Support System for Movie Production Company

Abstract

Like other major innovations, movie industry is growing rapidly and has gained immense importance world-wide. Billions of dollars are invested in this sector to make a successful movie. It becomes a matter of concern to propose an algorithm that can act as a decision support system for the production company to make a wise investment at the earliest stage of production. Various factors are used in calculating the success of a movie such as actors, directors, release date, user ratings, number of screens, gross, duration etc. On the basis of these factors, the current models are used for success prediction of a movie prior its release. The current model does not eliminate the risk of investment that is already done on making a movie. Our research recommends the right combination of actors, directors and the countries to release the movie prior the investment is made which will help in maximizing the profit of the production company. The proposed system uses hybrid algorithm to recommend based on the detailed analysis of the Box Office Mojo, Internet Movie Database (IMDb) and The Movies Dataset that uses both collaborative filtering and content-based filtering techniques for better recommendation.