Question: How best to prepare a dataset for sequential recommendation.

Me again :)

I would like to understand how best to prepare data for a sequential recommender similar to GRU4Rec.

Our data initially can be viewed as just a bunch of sequences of variable length, one sequence per user:

seqs = [
  [a, b, a, c, d, e, d, g],
  ...
]

Now I assume we want to prepare a y or target, so the simplest approach (as I see it) is to:

• slice off the last element of each item in seqs as the y
• pad the seqs to a max length

But then there are many ways in which we could make different versions of this dataset, for example from seqs[0], we could actually make many rows by creating copies incrementally predicting the next item.

So my question is:

• is there any theoretical best practice on how to prep such a dataset?
• are there are helpers in terms of implementation to make this easier / cleaner within TFRS or a TF data pipeline?
• edit: is there also a specific way to prepare data and formulate the problem in order to predict not the next sequence value but further ahead?

Thanks in advance!