Most collaborative filtering systems, such as matrix factorization, use vector representations for items and users. Those representations are deterministic, and do not allow modeling the uncertainty of the learned representation, which can be useful when a user has a small number of rated items (cold start), or when there is conflicting information about the behavior of a user or the ratings of an item. In this paper, we leverage recent works in learning Gaussian embeddings for the recommendation task. We show that this model performs well on three representative collections (Yahoo, Yelp and MovieLens) and analyze learned representations.