Performance Comparison of Algorithms for Movie Rating Estimation
This is an incremental comparison of existing methods for movie recommendation systems.
The paper compared three algorithms for predicting movie ratings from user-movie matrices, finding no significant performance differences and noting that Iterative Matrix Factorization performed well despite its simplicity.
In this paper, our goal is to compare performances of three different algorithms to predict the ratings that will be given to movies by potential users where we are given a user-movie rating matrix based on the past observations. To this end, we evaluate User-Based Collaborative Filtering, Iterative Matrix Factorization and Yehuda Koren's Integrated model using neighborhood and factorization where we use root mean square error (RMSE) as the performance evaluation metric. In short, we do not observe significant differences between performances, especially when the complexity increase is considered. We can conclude that Iterative Matrix Factorization performs fairly well despite its simplicity.