In the basic literature for recommender systems collaborative and content based filters are often referenced. Whilst collaborative filters often use similarity of items or users to predict items, content filters use item features to find items similar to what the user has already liked.
Is such a distinction relevant in tensorflow retrieval and ranking models here Recommending movies: retrieval | TensorFlow Recommenders, or do these models act as both collaborative and content based filters if the schemas supplied include item features?
Additionally, in basic recommender systems, content based filters are often used to overcome the cold start problem. How do tensorflow retrieval and ranking recommender systems overcome this?