  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.
