To accurately and actively provide users with  potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely used recommendation algorithms, whereas it is suffering the issue of data sparsity that severely degrades recommendation quality. To address this issue, we propose a collaborative filtering recommendation algorithm based on bipartite graph partitioning co-clustering, called BPCF. Firstly, users and items are constructed into a bipartite graph for co-clustering, which is then mapped to the low-dimensional feature space. Then, the proposed algorithm computes the two types of improved similarities (cluster preference similarity and rating similarity) according to the clustering results and combines them. Based on the combined similarity, the user-based approach and item-based approach are adopted, respectively, to predict for an unknown target rating and these prediction results are fused. Experimental results show that the proposed method outperforms the state-of-the-art co-clustering collaborative filtering recommendation algorithms.