Matrix Factorizaiton techniques are the most popular and effective choice for implementing Collaborative Filtering in Recommendation Systems. In the process of learning various Matrix Factorization algorithms, I implemented Variational Inference Matrix Factorizaiton introduced in:
Lim, Y. J., & Teh, Y. W. (2007). Variational Bayesian approach to movie rating prediction. Proceedings of KDD Cup and Workshop.
When applying this implementation on MovieLens dataset and randomly select 10% data as test set. I achieved the following RMSE (Average of 5 runs):
MovieLens 100k, RMSE = 0.9039 MovieLens 1m, RMSE = 0.8384 MovieLens 10m, RMSE = 0.7774