In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes ranking instead of rating. We use structured output prediction to optimize for specific non-uniform ranking scores. Experimental results show that our method gives very good ranking scores and scales well on collaborative filtering tasks.