research-article

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

Authors:

Noam Koenigstein,

Nir ZabariAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 316 - 326

https://doi.org/10.1145/3523227.3546763

Published: 13 September 2022 Publication History

Abstract

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.

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Cited By

Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/36528633:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3652863
Coppens IDe Pessemier TMartens L(2024)Balancing Habit Repetition and New Activity Exploration: A Longitudinal Micro-Randomized Trial in Physical Activity RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691715(1147-1151)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691715
Li JSun AMa WSun PZhang M(2024)Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food DeliveryProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688119(643-653)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688119
Show More Cited By

Index Terms

Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization
  2. Information systems applications
    1. Decision support systems

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 ACM.

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Published: 13 September 2022

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/36528633:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3652863
Coppens IDe Pessemier TMartens L(2024)Balancing Habit Repetition and New Activity Exploration: A Longitudinal Micro-Randomized Trial in Physical Activity RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691715(1147-1151)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691715
Li JSun AMa WSun PZhang M(2024)Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food DeliveryProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688119(643-653)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688119
Li MLiu YJullien SAriannezhad MYates AAliannejadi Mde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657835(924-934)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657835
Benamara IViennet E(2024)Power of Suggestion: Strategic Feature Manipulation in Transformer-Based Models2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI62512.2024.00033(174-180)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ICTAI62512.2024.00033
Jansen LBennin Kvan Kleef EVan Loo E(2024)Online grocery shopping recommender systemsComputers in Human Behavior10.1016/j.chb.2024.108336159:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.chb.2024.108336
Liu YLi MAriannezhad MMansoury MAliannejadi Mde Rijke M(2024)Measuring Item Fairness in Next Basket Recommendation: A Reproducibility StudyAdvances in Information Retrieval10.1007/978-3-031-56066-8_18(210-225)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56066-8_18
Yu YYang EGuo GJiang LWang XElkind E(2023)Basket representation learning by hypergraph convolution on repeated items for next-basket recommendationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/268(2415-2422)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/268
Romanov ALashinin OAnanyeva MKolesnikov S(2023)Time-Aware Item Weighting for the Next Basket RecommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608859(985-992)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608859
Sun WXie RZhang JZhao WLin LWen J(2023)Generative Next-Basket RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608823(737-743)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608823
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