Exploring Machine Learning Approaches for IMDb Movie Rating Prediction and Recommendation

Abstract

In the film industry, movie ratings are now a vital predictor of box office success. For personalized recommendation systems, which rely on reliable and effective models to produce accurate results, accurate prediction of these ratings is also essential. Using the IMDb dataset, this study investigates the use of machine learning techniques in movie rating analysis and recommendation systems to forecast movie rating categories and produce customized movie recommendations. A brief comparison with deep learning architectures is also provided. The results of this study provide a useful resource for future developments in movie rating prediction, assisting practitioners and researchers in enhancing recommendation systems’ efficacy.