  Over the past decade, tremendous progress has been made in Recommender
Systems (RecSys) for well-known tasks such as next-item and next-basket
prediction. On the other hand, the recently proposed next-period recommendation
(NPR) task is not covered as much. Current works about NPR are mostly based
around distinct problem formulations, methods, and proprietary datasets, making
solutions difficult to reproduce. In this article, we aim to fill the gap in
RecSys methods evaluation on the NPR task using publicly available datasets and
(1) introduce the TTRS, a large-scale financial transactions dataset suitable
for RecSys methods evaluation; (2) benchmark popular RecSys approaches on
several datasets for the NPR task. When performing our analysis, we found a
strong repetitive consumption pattern in several real-world datasets. With this
setup, our results suggest that the repetitive nature of data is still hard to
generalize for the evaluated RecSys methods, and novel item prediction
performance is still questionable.
