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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2022: Machine Learning and Knowledge Discovery in Databases pp 574–589Cite as

Basket Booster for Prototype-based Contrastive Learning in Next Basket Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13713))

Abstract

Next basket recommendation seeks to model the correlation of items and mine users’ interests hidden in basket sequences, and tries to infer a set of items that tend to be adopted in the next session with the mined information. However, the feedback provided by users often involves only a small fraction of millions to billions of items. Sparse data makes it hard for model to infer high-quality representations for basket sequences, which further leads to poor recommendation. Inspired by the recent success of representation learning in some fields, e.g., computer vision and clustering, we propose a basket booster for prototype-based contrastive learning (BPCL) in next basket recommendation. A correlative basket booster is designed to mine self-supervised signals just from raw data and make augmentation for baskets. To our best knowledge, this is the first work to promote learning of prototype representation through basket augmentation, which helps overcome the difficulties caused by data sparsity and leads to a better next basket recommendation performance. Extensive experiments on three public real-world datasets demonstrate that the proposed BPCL method achieves better performance than the existing state-of-the-art methods.

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Notes

  1. 1.

    http://www.delicious.com.

  2. 2.

    https://www.kaggle.com/skillsmuggler/amazon-ratings.

  3. 3.

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

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Acknowledgments

This work was supported by NSFC (61876193) and Guangdong Province Key Laboratory of Computational Science at Sun Yat-sen University (2020B1212060032).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Ting-Ting Su, Zhen-Yu He, Man-Sheng Chen & Chang-Dong Wang

  2. Guangdong Province Key Laboratory of Computational Science, Guangzhou, China

    Ting-Ting Su, Zhen-Yu He, Man-Sheng Chen & Chang-Dong Wang

  3. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, China

    Ting-Ting Su, Zhen-Yu He, Man-Sheng Chen & Chang-Dong Wang

Authors
  1. Ting-Ting Su
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  2. Zhen-Yu He
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  3. Man-Sheng Chen
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  4. Chang-Dong Wang
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Corresponding author

Correspondence to Chang-Dong Wang .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d’Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Su, TT., He, ZY., Chen, MS., Wang, CD. (2023). Basket Booster for Prototype-based Contrastive Learning in Next Basket Recommendation. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_35

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  • DOI: https://doi.org/10.1007/978-3-031-26387-3_35

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  • Print ISBN: 978-3-031-26386-6

  • Online ISBN: 978-3-031-26387-3

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