Personalized Cadence Awareness for Next Basket Recommendation

Abstract

This empirical study addresses the problem of Next Basket Repurchase Recommendation (NBRR), an often overlooked aspect of Next Basket Recommendation (NBR). While NBR aims to suggest items for a user’s next basket based on their prior basket history, NBRR focuses solely on recommending items previously purchased by the user. Despite the common ground between NBR and NBRR, the latter requires a distinct approach. In this paper, we survey recent developments in the fields of NBR and NBRR, emphasizing the different strategies employed for these closely related challenges. In addition, we review the common characteristics of users’ repurchase patterns, which characterize the NBRR problem. Building on these insights, we introduce a novel hyper-convolutional model tailored to capture behavioral patterns associated with repeated purchases. To evaluate its effectiveness, we conduct experiments on three publicly available datasets, offering a comprehensive analysis across three levels of granularity: user-level, order-level, and item-level. Our analysis illuminates the conditions under which the model excels and identifies scenarios where it may encounter challenges. This research contributes valuable insights into enhancing repurchase recommendation systems and advancing the understanding of user purchase behavior in general.