Recently, deep neural networks have been widely applied to recommender systems. A representative work is to utilize deep learning for modeling complex user-item interactions. However, similar to traditional latent factor models by factorizing user-item interactions, they tend to be ineffective to capture localized information. Localized information, such as neighborhood between items and users, is important to recommender systems in complementing the user-item interaction data. To incorporate neiborhood information, we propose a novel Neighborhood-based Neural Collaborative Filtering model (NNCF). Extensive experiments on three real-world online purchase datasets demonstrate the effectiveness of our model for the implicit recommendation task.