  Within-basket recommendation (WBR) refers to the task of recommending items
to the end of completing a non-empty shopping basket during a shopping session.
While the latest innovations in this space demonstrate remarkable performance
improvement on benchmark datasets, they often overlook the complexity of user
behaviors in practice, such as 1) co-existence of multiple shopping intentions,
2) multi-granularity of such intentions, and 3) interleaving behavior
(switching intentions) in a shopping session. This paper presents Neural
Pattern Associator (NPA), a deep item-association-mining model that explicitly
models the aforementioned factors. Specifically, inspired by vector
quantization, the NPA model learns to encode common user intentions (or
item-combination patterns) as quantized representations (a.k.a. codebook),
which permits identification of users's shopping intentions via
attention-driven lookup during the reasoning phase. This yields coherent and
self-interpretable recommendations. We evaluated the proposed NPA model across
multiple extensive datasets, encompassing the domains of grocery e-commerce
(shopping basket completion) and music (playlist extension), where our
quantitative evaluations show that the NPA model significantly outperforms a
wide range of existing WBR solutions, reflecting the benefit of explicitly
modeling complex user intentions.
