What is the best way to manage repetitveness of recommendations in a tensorflow recommender system?

In a recommender system, what is the best way to manage repetiveness of items being recommended?

This may come about, for example, if the user provides no additional input and so the same items are recommended. In some cases, re-recommended items are desirable to give the user more time to consider the item.

What is the best way to manage the trade off between items being under and over recommended in a tensorflow recommender system? Do you manage this in tensorflow itself or do you need a custom filter afterwards?