Predicting movies user ratings with IMDb attributes

Abstract

In the era of Web 2.0, consumers share their ratings or comments easily with other people after watching a movie. User rating simplified the procedure which consumers express their opinions about a product, and is a great indicator to predict the box office [1-4]. This study develops user rating prediction models which used classification technique (linear combination, multiple linear regression, neural networks) to develop. Total research dataset included 32968 movies, 31506 movies were training data, and others were testing data. Three of research findings are worth summarizing: first, the prediction absolute error of three models is below 0.82, it represents the user ratings are well-predicted by the models; second, the forecast of neural networks prediction model is more accurate than others; third, some predictors profoundly affect user rating, such as writers, actors and directors. Therefore, investors and movie production companies could invest an optimal portfolio to increase ROI.