Movie Rating Prediction using Convolutional Neural Network based on Historical Values

Movie rating is an important element to decide movie quality. It is like a summary to reflect the quality of all element inside a movie. People prefer to use rating as reference to decide before deciding to watch a movie or not. It is important to predict movie rating before it is released to maintain the objectivity of the movie rating. Many existing researches failed to address this problem because they used the post-release elements such as social media comments to predict movie rating. The other problem is the predicted rating is not intended for general people. Several researches used collaborative filtering, however the rating found was intended for specific people. To address limitations from previous researches, this study used historical values of the movie as features. Historical values could be generated from pre-released elements from the movie, it was created from relation between movie which based on movie similar attributes such as actor, director, genres, content rating, and production companies. By using historical values, objective prediction can be made even before the movie released. The proposed method was intended to make more accurate and general prediction for movie rating. In this study, usage of historical features and convolutional neural network (CNN) as model showed promising result.