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Understanding and Learning from User Behavior for Recommendation in Multi-channel Retail

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Advances in Information Retrieval (ECIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13186))

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Abstract

Online shopping is gaining more and more popularity everyday. Traditional retailers with physical stores adjust to this trend by allowing their customers to shop online as well as offline, i.e., in-store. Increasingly, customers can browse and purchase products across multiple shopping channels. Understanding how customer behavior relates to the availability of multiple shopping channels is an important prerequisite for many downstream machine learning tasks, such as recommendation and purchase prediction. However, previous work in this domain is limited to analyzing single-channel behavior only. In this project, we first provide a better understanding of the similarities and differences between online and offline behavior. We further study the next basket recommendation task in a multi-channel context, where the goal is to build recommendation algorithms that can leverage the rich cross-channel user behavior data in order to enhance the customer experience.

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Authors and Affiliations

  1. AIRLab, University of Amsterdam, Amsterdam, The Netherlands

    Mozhdeh Ariannezhad

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  1. Mozhdeh Ariannezhad
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Correspondence to Mozhdeh Ariannezhad .

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Editors and Affiliations

  1. Martin Luther University Halle-Wittenberg, Halle, Germany

    Matthias Hagen

  2. Leiden University, Leiden, The Netherlands

    Suzan Verberne

  3. University of Glasgow, Glasgow, UK

    Craig Macdonald

  4. University of Duisburg-Essen, Essen, Germany

    Christin Seifert

  5. University of Stavanger, Stavanger, Norway

    Krisztian Balog

  6. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  7. University of Stavanger, Stavanger, Norway

    Vinay Setty

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Ariannezhad, M. (2022). Understanding and Learning from User Behavior for Recommendation in Multi-channel Retail. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_56

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  • DOI: https://doi.org/10.1007/978-3-030-99739-7_56

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-99739-7

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