A recommendation model initially increased customer purchases but its impact has declined over time, while the deployed model has not changed. What should you do to improve performance?

The correct approach is to periodically retrain the model with new data. As customer preferences and inventory evolve, the model's understanding of optimal recommendations becomes outdated, leading to performance degradation. Incorporating recent data allows the model to learn these new patterns and maintain its effectiveness. Rebuilding the model from scratch without new data won't address the underlying issue of data drift. Tuning hyperparameters might offer minor improvements but won't resolve the fundamental problem of the model operating on stale information. Retraining with only original data and adding regularization also fails to account for the dynamic nature of inventory and customer behavior.