<p>Neural collaborative filtering (NCF)-based recommendation models have been widely adopted in practical recommender systems due to their effectiveness. However, these models are typically developed under the static deep learning paradigm, where training is conducted on fixed datasets with the implicit assumption of a static data distribution. This approach is ill-suited for dynamic environments, such as those encountered in real-world platforms, where user preferences and collaborative filtering patterns evolve continuously. To address this limitation, incremental learning-a paradigm designed to integrate new knowledge while preserving previously learned information-emerges as a promising alternative. Despite its potential, the direct application of conventional incremental learning methods, which are prevalent in domains like computer vision and natural language processing, is hindered by unique challenges in recommender systems. These include the distinct task paradigm, data complexity, and sparsity issues. Moreover, existing incremental learning approaches tailored for neural recommendation models remain scarce and often suffer from limited generalizability. To bridge this gap, we propose an innovative experience replay-based incremental learning framework specifically designed for neural recommendation models, termed Replay Samples with Maximally Extreme GGscore (MEGG). At the core of MEGG is a novel metric, the GGscore, which quantifies the influence of individual samples on model training. By selectively replaying samples with the most extreme GGscores, our method effectively mitigates catastrophic forgetting, thereby maintaining high predictive performance over time. A key advantage of MEGG lies in its data-centric nature, which renders it agnostic to the underlying model architecture. This ensures broad applicability across various neural recommendation models and seamless integration with existing incremental learning frameworks to further enhance performance. Extensive experiments conducted on three neural recommendation models across four benchmark datasets demonstrate the superior effectiveness of MEGG compared to state-of-the-art methods. Furthermore, additional evaluations highlight its scalability, efficiency, and robustness. The implementation of MEGG will be made publicly available upon acceptance.</p>