short-paper

Generative Next-Basket Recommendation

Authors:

Wayne Xin Zhao,

Ji-Rong WenAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 737 - 743

https://doi.org/10.1145/3604915.3608823

Published: 14 September 2023 Publication History

Abstract

Next-basket Recommendation (NBR) refers to the task of predicting a set of items that a user will purchase in the next basket. However, most of existing works merely focus on the correlations between user preferences and predicted items, ignoring the essential correlations among items in the next basket, which often results in over-homogenization of predicted items. In this work, we presents a Generative next-basket Recommendation model (GenRec), a novel NBR paradigm that generates the recommended items one by one to form the next basket via an autoregressive decoder. This generative NBR paradigm contributes to capturing and considering item correlations inside each baskets in both training and serving. Moreover, we jointly consider user’s both item- and basket-level contextual information to better capture user’s multi-granularity preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model.

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Cited By

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

Index Terms

Generative Next-Basket Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 ACM.

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Published: 14 September 2023

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

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

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Cited By

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

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