  Next-basket recommendation considers the problem of recommending a set of
items into the next basket that users will purchase as a whole. In this paper,
we develop a novel mixed model with preferences, popularities and transitions
(M2) for the next-basket recommendation. This method models three important
factors in next-basket generation process: 1) users' general preferences, 2)
items' global popularities and 3) transition patterns among items. Unlike
existing recurrent neural network-based approaches, M2 does not use the
complicated networks to model the transitions among items, or generate
embeddings for users. Instead, it has a simple encoder-decoder based approach
(ed-Trans) to better model the transition patterns among items. We compared M2
with different combinations of the factors with 5 state-of-the-art next-basket
recommendation methods on 4 public benchmark datasets in recommending the
first, second and third next basket. Our experimental results demonstrate that
M2 significantly outperforms the state-of-the-art methods on all the datasets
in all the tasks, with an improvement of up to 22.1%. In addition, our ablation
study demonstrates that the ed-Trans is more effective than recurrent neural
networks in terms of the recommendation performance. We also have a thorough
discussion on various experimental protocols and evaluation metrics for
next-basket recommendation evaluation.
