Matrix factorization (MF) based approaches have proven to be efficient for rating-based recommendation systems. In this paper, we propose a hybrid approach that alloys an improved MF and the so-called NSVD1 approach, resulting in a very accurate factor model. After that, we propose a unification of factor models and neighbor based approaches, which further improves the performance. The approaches are evaluated on the Netflix Prize dataset, and they provide very low RMSE, and favorable running time. Our best solution presented here with Quiz RMSE 0.8851 outperforms all published single methods in the literature. ©2008 IEEE.