Recommendation systems address the "information overload" problem by filtering out items that customers may not be interested. Collaborative Filtering (CF) is the most popular technique which recommends items to a user based on similar users' preferences and/or similar items records by utilizing the user-item rating matrix. However, such a large amount of explicit feedbacks are not always available. Furthermore, user preferences are changing over time. The Conventional CF cannot capture the temporal dynamics of recommendations well. In this paper, we apply Deep Recurrent Neural Networks (DRNNs) to CF and generate dynamic, personalized recommendations by utilizing user purchase history big data. Experiments on the MovieLens dataset show relative improvements over previously reported results.