With the rapid development of cloud manufacturing (CMfg), a large number of functionally equivalent cloud services are available on the Internet, thus quality of service (QoS) prediction has become a hot issue. Since the QoS of service varies widely among users, many Collaborative Filtering (CF) approaches are recently proposed to predict the unknown QoS by extending the Pearson Correlation Coefficient (PCC) similarity. However, most existing approaches either enhance the similarity computation inadequately or ignore the data sparsity problem, and are thus vulnerable to the unreliable result. To address this problem, we propose a hybrid approach HAP for QoS prediction. First, to achieve high prediction accuracy, we employ a similarity-enhanced CF (L-CF) for local QoS prediction, in which the personalized influence of similar users and services are considered when computing PCC similarity. Second, to overcome the data sparsity problem, we propose a global QoS prediction approach based on the case-based reasoning (G-CBR) which could make full use of both user and service information. Finally, we establish an ensemble model to combine the results of two approaches. Comprehensive real-world experiments are conducted to demonstrate the effectiveness of our approach compared with other state-of-the-arts approaches, particularly when the QoS data is very sparse.