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

Ori Katz, Oren Barkan, Nir Zabari, Noam Koenigstein

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء


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.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems
ناشرAssociation for Computing Machinery, Inc
عدد الصفحات11
رقم المعيار الدولي للكتب (الإلكتروني)9781450392785
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 12 سبتمبر 2022
الحدث16th ACM Conference on Recommender Systems, RecSys 2022 - Seattle, الولايات المتّحدة
المدة: ١٨ سبتمبر ٢٠٢٢٢٣ سبتمبر ٢٠٢٢

سلسلة المنشورات

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


!!Conference16th ACM Conference on Recommender Systems, RecSys 2022
الدولة/الإقليمالولايات المتّحدة

ملاحظة ببليوغرافية

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© 2022 ACM.

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