MicroRNAs (miRNAs) play important roles in the pathogenesis and development of many complex diseases. The experimental confirmation of disease-related miRNAs is costly and time-consuming. An efficient and accurate computational model for identifying potential miRNA–disease associations is a useful supplement for experimental approaches. In this study, we develop a new method for measuring miRNA and disease similarities, which are key issues in identifying miRNA–disease associations, based on normalized mutual information. Subsequently, a network-based collaborative filtering recommendation model, network-based collaborative filtering (NetCF), is proposed for predicting potential miRNA–disease associations by integrating miRNA and disease similarities along with experimentally verified miRNA–disease associations. Leave-one-out cross validation is implemented to evaluate the predicted performance of NetCF. NetCF obtains a reliable AUC value of 0.8960, which is superior to other competitive methods. Implementing NetCF to predict lung cancer and prostate cancer-related miRNAs, 94% of the top 50 predicted miRNAs of each cancer have been confirmed by other databases.