short-paper

Time-Aware Item Weighting for the Next Basket Recommendations

Authors:

Aleksey Romanov,

Marina Ananyeva,

Sergey KolesnikovAuthors Info & Claims

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

Pages 985 - 992

https://doi.org/10.1145/3604915.3608859

Published: 14 September 2023 Publication History

Abstract

In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better performance. However, many of them do not use information about the time of prediction and time intervals between baskets. To fill this gap, we propose a novel method, Time-Aware Item-based Weighting (TAIW), which takes timestamps and intervals into account. We provide experiments on three real-world datasets, and TAIW outperforms well-tuned state-of-the-art baselines for next-basket recommendations. In addition, we show the results of an ablation study and a case study of a few items.

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Cited By

Liu YLiu MWalder CXie LSerra ESpezzano F(2024)A Universal Sets-level Optimization Framework for Next Set RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679610(1544-1554)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679610

Index Terms

Time-Aware Item Weighting for the Next Basket Recommendations
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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 14 September 2023

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RecSys '23

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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Cited By

Liu YLiu MWalder CXie LSerra ESpezzano F(2024)A Universal Sets-level Optimization Framework for Next Set RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679610(1544-1554)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679610

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