  Next-basket recommendation (NBR) is prevalent in e-commerce and retail
industry. In this scenario, a user purchases a set of items (a basket) at a
time. NBR performs sequential modeling and recommendation based on a sequence
of baskets. NBR is in general more complex than the widely studied sequential
(session-based) recommendation which recommends the next item based on a
sequence of items. Recurrent neural network (RNN) has proved to be very
effective for sequential modeling and thus been adapted for NBR. However, we
argue that existing RNNs cannot directly capture item frequency information in
the recommendation scenario.
  Through careful analysis of real-world datasets, we find that {\em
personalized item frequency} (PIF) information (which records the number of
times that each item is purchased by a user) provides two critical signals for
NBR. But, this has been largely ignored by existing methods. Even though
existing methods such as RNN based methods have strong representation ability,
our empirical results show that they fail to learn and capture PIF. As a
result, existing methods cannot fully exploit the critical signals contained in
PIF. Given this inherent limitation of RNNs, we propose a simple item frequency
based k-nearest neighbors (kNN) method to directly utilize these critical
signals. We evaluate our method on four public real-world datasets. Despite its
relative simplicity, our method frequently outperforms the state-of-the-art NBR
methods -- including deep learning based methods using RNNs -- when patterns
associated with PIF play an important role in the data.
