Within-basket Recommendation via Neural Pattern Associator

• URL: http://arxiv.org/abs/2401.16433v2
• Date: Thu, 14 Mar 2024 21:08:34 GMT
• Title: Within-basket Recommendation via Neural Pattern Associator
• Authors: Kai Luo, Tianshu Shen, Lan Yao, Ga Wu, Aaron Liblong, Istvan Fehervari, Ruijian An, Jawad Ahmed, Harshit Mishra, Charu Pujari,
• Abstract summary: Within-basket recommendation (WBR) refers to the task of recommending items to the end of completing a non-empty shopping basket. This paper presents Neural Pattern Associator (NPA), a deep item-association-mining model that explicitly models user intentions.
• Score: 6.474720465174676
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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