Madasu V Bhagya Sree, Suthari Vamshi, Eppalapally Saikiran, Najeema Afrin
Content-based, recommendation system, expert systems, deep learning, audience groups.
The movie is an important part of our daily entertainment. For the worldwide movie industry, this is a highly thriving and substantial sector that attracts attention from people of all ages. In a recent study, it has been observed that only a small number of films are successful. The film production industry's stakeholders have been extremely stressed by uncertainty in the sector There is a growing belief among filmmakers and researchers that certain expert systems must be established to predict the movie's success probability in advance of its production with sufficient accuracy. In order to anticipate film popularity at the final production stage, a large amount of research has been carried out. We need to predict at the early stages of film production and provide specific information on upcoming movies, so that movie makers can estimate their future films and make necessary changes. The study suggests that a content Based Recommendation System for movies, CBRS using Preliminary Film features such as genre, cast, director, keyword and movie description, should be developed. We have created a new set of features and proposed the Random Forest deep learning (DL) model for building an multiclass movie popularity prediction system by using RS data, film ratings and voting information from similar movies. We've also made proposals for a system of predicting the popularity of an upcoming film according to different audience groups. The audience group was divided into four age groups: Junior, Senior, Middle and Elderly. The publicly available internet movie database IMDb and the film database HTTP(TMDb) data were used in this study. We have implemented a multiclass classification model that has yielded 96.8% accuracy which is superior to all the reference models. The potential of predictive and prescriptive data analysis within IT systems to underpin industry decisions has been highlighted in this study.
Article Details
Unique Paper ID: 159115

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 336 - 341
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