  The expansion of streaming media and e-commerce has led to a boom in
recommendation systems, including Sequential recommendation systems, which
consider the user's previous interactions with items. In recent years, research
has focused on architectural improvements such as transformer blocks and
feature extraction that can augment model information. Among these features are
context and attributes. Of particular importance is the temporal footprint,
which is often considered part of the context and seen in previous publications
as interchangeable with positional information. Other publications use
positional encodings with little attention to them. In this paper, we analyse
positional encodings, showing that they provide relative information between
items that are not inferable from the temporal footprint. Furthermore, we
evaluate different encodings and how they affect metrics and stability using
Amazon datasets. We added some new encodings to help with these problems along
the way. We found that we can reach new state-of-the-art results by finding the
correct positional encoding, but more importantly, certain encodings stabilise
the training.
