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You are a machine learning engineer working for an e-commerce company. You have developed a recommendation model that predicts products customers are likely to buy based on their browsing history and past purchases. The model initially performs well, but after deploying it in production, you notice 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. Given these challenges, which of the following strategies is the MOST LIKELY to help prevent overfitting and catastrophic forgetting while maintaining model accuracy?