Excluding previously seen items from test recommendations

In other libraries (e.g. Lightfm) it's common to have a facility to exclude previously seen items from test\eval reccomendations by passing in a set of train_interactions into our evaluation method (see lightfm approach). This mimics what we would do in production and as such gives us a more accurate read evaluation results in our offline training. Without this ability we would find it hard to know if we are overfitting on our training data as the eval results become a lot less meaningful when your real performance is being masked.

I noticed in the tutorials provided it explicitly states that this is approach has not been adopted for TFRS and that we should "appropriately specify models to learn this behaviour automatically".

1. What does an appropriately specified model in this instance look like? (e.g. do we need to capture sequence behaviour via RNNS (e.g. like in this paper)? Should including timestamp data as context info in our queries capture this behaviour?)..... is there an intention to provide more details on this?

2. Is it envisaged that a provision will be made to exclude previously interacted with items (or is this a decision that's dictated by the modelling approach?)