How does TensorFlow handle cold start problems in recommendation models?

Cold start occurs when the model lacks sufficient historical data for new users or items.

TensorFlow recommendation systems rely heavily on historical interactions. For new users/items, embeddings are poorly trained or random. This leads to poor initial predictions. Solutions include hybrid models, content-based features, or pre-trained embeddings.