Within-basket Recommendation via Neural Pattern Associator
This work improves recommendation systems for online shopping and music streaming by modeling complex user intentions, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of within-basket recommendation by addressing overlooked complexities in user behaviors, such as multiple shopping intentions and interleaving actions, and introduces the Neural Pattern Associator (NPA) model, which significantly outperforms existing solutions on benchmark datasets in e-commerce and music domains.
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.