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.

这篇论文介绍了神经模式关联器（NPA），这是一种深度商品关联挖掘模型，明确地模拟了用户行为的复杂性，通过量化表示常见的用户目的（或商品组合模式），在推理阶段通过注意力驱动的查找来识别用户的购物意图，从而产生连贯且可解释的推荐结果。在杂货电子商务（购物篮完成）和音乐（播放列表扩展）等多个领域的广泛数据集上评估了提出的 NPA 模型，定量评估结果显示 NPA 模型明显优于多种现有的购物篮推荐解决方案，反映了明确模拟复杂用户购物意图的好处。