Rating Prediction on Movie Recommendation System: Collaborative Filtering Algorithm (CFA) vs. Dissymetrical Percentage Collaborative Filtering Algorithm (DSPCFA)

Abstract

Recommendation system is one of many solutions for getting information rapidly from the many data available and one of its applications is the movie recommendation system. Movie recommendation system filters information then recommends movies based on rating preferences or user information. One of the most widely used algorithms is the user based collaborative filtering algorithm (CFA) to predict movie ratings which will be recommended based on similarity between target user and other users regardless of common items or the number of movies that have been rated by both. One different approach of the CFA algorithm is a dissymmetrical percentage collaborative filtering algorithm (DSPCFA) that involves common items as a consideration of measuring similarity. This study also uses two similarity measurement methods, namely the pearson correlation similarity method and the cosine similarity method as a comparison to determine the characteristics of each measurement method. The experiment results show that the DSPCFA algorithm produces a lower error value than the CFA algorithm with an error decrease of about 5% for the RMSE evaluation method (Root-mean Squared Error) and an error decrease of about 7% using the MAE (Mean Absolute Error) evaluation method. While measurement method tested shows that the pearson correlation similarity method produces a lower error value than the cosine similarity method.