Star Profile and Audience Interest-Based Movie Rating Prediction Model

In today's entertainment era, several movies are produced over a year. We all try to get a review of any movie before planning to watch it. Much work has been done in the past for early movie success prediction but most of them are from the production perspective. However, the audience perspective is still unexplored. Further, most of the predictive models work with some basic features such as genre, cast, comments, reviews, and production house. However, a few important factors such as current audience interest shift, the social influence of the star, and personal liking of a person are much more important in predicting the rating of a movie. In this work, we propose an advanced movie rating prediction cross model, which could rate movies according to general as well as some fine-tuned features. This helps in providing a fair prediction of a movie according to global and personal audience interests. We collected a wide range of data from the Web, and social media along with the IMDb dataset. A comparative analysis of the proposed model with existing benchmark schemes has been done. Significant improvement in movie rating and success prediction is observed with an accuracy of 87%.