  At the present time, sequential item recommendation models are compared by
calculating metrics on a small item subset (target set) to speed up
computation. The target set contains the relevant item and a set of negative
items that are sampled from the full item set. Two well-known strategies to
sample negative items are uniform random sampling and sampling by popularity to
better approximate the item frequency distribution in the dataset. Most
recently published papers on sequential item recommendation rely on sampling by
popularity to compare the evaluated models. However, recent work has already
shown that an evaluation with uniform random sampling may not be consistent
with the full ranking, that is, the model ranking obtained by evaluating a
metric using the full item set as target set, which raises the question whether
the ranking obtained by sampling by popularity is equal to the full ranking. In
this work, we re-evaluate current state-of-the-art sequential recommender
models from the point of view, whether these sampling strategies have an impact
on the final ranking of the models. We therefore train four recently proposed
sequential recommendation models on five widely known datasets. For each
dataset and model, we employ three evaluation strategies. First, we compute the
full model ranking. Then we evaluate all models on a target set sampled by the
two different sampling strategies, uniform random sampling and sampling by
popularity with the commonly used target set size of 100, compute the model
ranking for each strategy and compare them with each other. Additionally, we
vary the size of the sampled target set. Overall, we find that both sampling
strategies can produce inconsistent rankings compared with the full ranking of
the models. Furthermore, both sampling by popularity and uniform random
sampling do not consistently produce the same ranking ...
