Conditional neural process (CNP) has been extensively applied into data analyzing tasks due to its excellent ability to make accurate predictions for incomplete data points. However, in literature there are only few works that studied the CNPin recommendation systems. In this work, we propose CCFP, which is a collaborative filtering method that differs from other CF models by incorporating CNP into encoder-decoder architecture. By analyzing the complete user-item interaction data, our model fits a global representation that can better rep-resenting the features of users and items. CCFP can significantly improve the recommendation performance compared to baselines by predicting items for the target users with their incomplete observation data.