Recommender systems have been applied in many domains to solve the information-overload problem, and most of them make recommendations based on explicit data which expressed ratings in different scores. However, there are a lot of implicit data in the real world, such as users' purchase history, click history, browsing activity and so on, and it is difficult to find users' preferences based on this kind of data. In this work, we proposed a novel recommendation method, which incorporates topic model and matrix factorization. The content of documents and similar users' preferences are used to predict the negative and positive examples. The proposed approach achieves better performance than other recommender systems with implicit feedback.