Using context information to improve the accuracy of recommendation systems and enhance user experience is one of the hottest topics in the domain of recommend systems. However the issue of data sparse still challenges the existing context-aware recommender system. To better alleviate the data sparse problem, this paper proposes a rating prediction method, i.e., joint matrix factorization with user category prefernce(JMF-UCP). Based on the joint matrix factorization, the method addresses the data sparse problem by combining user′s rating information and user category preference to predict the rating score with higher accuracy. The time complexity of the proposed method linearly increases with the number of amount of dataset and is scalable to very large datasets. Experimental results on real world rating dataset MovieLens demonstrate that the proposed method can achieve better accuracy.