Movie rating prediction using ensemble learning and mixed type attributes

Abstract

Nowadays, audience can easily share their rating about a movie on the internet. Predicting movie user ratings automatically is specifically valuable for prediction box office gross in the cinema sector. As a result, movie rating prediction has been a popular application area for machine learning researchers. Although most of the recent studies consider using mostly numerical features in analyses, handling nominal features is still an open problem. In this study, we propose a method for predicting movie user ratings based on numerical and nominal feature collaboration and ensemble learning. The effectiveness and the performance of the proposed approach is validated on Internet Movie Database (IMDb) performance dataset by comparing with different methods in the literature. Results show that, using mixed data types along with the ensemble learning improves the movie rating prediction.