In recommender systems, review generation is increasingly becoming an important task. Previously proposed neural models concatenate the user and item information to each timestep of an RNN to steer it towards generating their specific review. In this paper, we show how a student-teacher like architecture can be used to rapidly build a review generator with a low perplexity score.