You are a machine learning engineer at an e-commerce company. You developed a recommendation model that predicts products customers are likely to buy based on their browsing history and past purchases. Initially, the model performs well, but after deployment, you observe two issues: the model’s performance degrades over time as new data is added (catastrophic forgetting), and the model shows signs of overfitting during retraining on updated datasets. Which of the following strategies is MOST LIKELY to help prevent overfitting and catastrophic forgetting while maintaining model accuracy?