Hi Efrain,
I believe I solved this and so this might be what you are after, the example works on generating recommendations from the tutorials with the use_timestamp features:
index = tfrs.layers.factorized_top_k.BruteForce(model.query_model)
index.index_from_dataset(
tf.data.Dataset.zip((movies.batch(100), movies.batch(100).map(model.candidate_model)))
)
query = dict(pd.DataFrame({‘user_id’: [“42”],‘timestamp’:[879024327]}).iloc[0].map(lambda x: tf.expand_dims(x,axis=0)))

Get recommendations.
_, titles = index(query)
print(f"Recommendations for user 42: {titles[0, :5]}")
hopefully that works for you!