Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation

Ori Katz, Oren Barkan, Nir Zabari, Noam Koenigstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The problem of Next Basket Recommendation (NBR) addresses the challenge of recommending items for the next basket of a user, based on her sequence of prior baskets. In this paper, we focus on a variation of this problem in which we aim to predict repurchases, i.e. we wish to recommend a user only items she had purchased before. We coin this problem Next Basket Repurchase Recommendation (NBRR). Over the years, a variety of models have been proposed to address the problem of NBR, however, the problem of NBRR has been overlooked. Although being highly related problems, which are often solved by the same methods, the problem of repurchase recommendation calls for a different approach. In this paper, we share insights from our experience of facing the challenge of NBRR. In light of these insights, we propose a novel hyper-convolutional model to leverage the behavioral patterns of repeated purchases. We demonstrate the effectiveness of the proposed model on three publicly available datasets, where it is shown to outperform other existing methods across multiple metrics.

Original languageEnglish
Title of host publicationRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages11
ISBN (Electronic)9781450392785
StatePublished - 12 Sep 2022
Event16th ACM Conference on Recommender Systems, RecSys 2022 - Seattle, United States
Duration: 18 Sep 202223 Sep 2022

Publication series

NameRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems


Conference16th ACM Conference on Recommender Systems, RecSys 2022
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2022 ACM.


  • Collaborative Filtering
  • Next Basket Recommendation
  • Recommender Systems

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