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European Conference on Information Retrieval

ECIR 2023: Advances in Information Retrieval pp 502–511Cite as

Time-Dependent Next-Basket Recommendations

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13981))

Abstract

There are various real-world applications for next-basket recommender systems. One of them is guiding a website user who wants to buy anything toward a collection of items. Recent works demonstrate that methods based on the frequency of prior purchases outperform other deep learning algorithms in terms of performance. These techniques, however, do not consider timestamps and time intervals between interactions. Additionally, they often miss the time period that passes between the last known basket and the prediction time. In this study, we explore whether such knowledge could improve current state-of-the-art next-basket recommender systems. Our results on three real-world datasets show how such enhancement may increase prediction quality. These findings might pave the way for important research directions in the field of next-basket recommendations.

The contribution of D.I. Ignatov to the paper was done within the framework of the HSE University Basic Research Program.

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Notes

  1. 1.

    https://www.kaggle.com/datasets/frtgnn/dunnhumby-the-complete-journey.

  2. 2.

    https://www.kaggle.com/datasets/chiranjivdas09/ta-feng-grocery-dataset.

  3. 3.

    https://www.kaggle.com/competitions/instacart-market-basket-analysis/data.

  4. 4.

    https://optuna.org.

  5. 5.

    https://github.com/sergunya17/time_dependent_nbr.

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Author information

Authors and Affiliations

  1. Tinkoff, 2-Ya Khutorskaya Ulitsa, 38A, bld. 26, Moscow, 127287, Russian Federation

    Sergey Naumov, Marina Ananyeva, Oleg Lashinin & Sergey Kolesnikov

  2. National Research University Higher School of Economics, 20 Myasnitskaya St, Moscow, 101000, Russian Federation

    Marina Ananyeva & Dmitry I. Ignatov

  3. Moscow Institute of Physics and Technology, Institutskiy Pereulok, 9, Dolgoprudny, Moscow Oblast, 141701, Russian Federation

    Sergey Naumov

Authors
  1. Sergey Naumov
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  2. Marina Ananyeva
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  3. Oleg Lashinin
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  4. Sergey Kolesnikov
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  5. Dmitry I. Ignatov
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Corresponding author

Correspondence to Oleg Lashinin .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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Naumov, S., Ananyeva, M., Lashinin, O., Kolesnikov, S., Ignatov, D.I. (2023). Time-Dependent Next-Basket Recommendations. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981. Springer, Cham. https://doi.org/10.1007/978-3-031-28238-6_41

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  • DOI: https://doi.org/10.1007/978-3-031-28238-6_41

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  • Online ISBN: 978-3-031-28238-6

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