You stream users' changing fashion behavior into the model. How should you use incoming data to keep recommendations current?

Continuously retraining the model on a combination of existing and new data ensures the model learns from historical patterns while adapting to recent trends. This approach prevents "catastrophic forgetting" of older, still relevant patterns and keeps recommendations current with evolving fashion behaviors. Retraining on just new data would quickly discard valuable historical context. Using new data solely as a test set, or existing data as a test set for a model trained on new data, misses the opportunity to incorporate the latest information into the model's learning, leading to stale recommendations.