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    Micro-behavior encoding for session-based recommendation

    Yuan, J. and Ji, W. and Zhang, Dell and Pan, J. and Wang, X. (2022) Micro-behavior encoding for session-based recommendation. In: UNSPECIFIED (ed.) Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE). IEEE. (In Press)

    mbe_icde22_accepted.pdf - Author's Accepted Manuscript

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    Session-based recommendation (SR) aims to predict the next item for recommendation based on previously recorded sessions of user interaction. The majority of existing approaches to SR focus on modeling the transition patterns of items. In such models, the so-called micro-behaviors describing how the user locates an item and carries out various activities on it (e.g., click, add-to-cart, and read-comments), are simply ignored. A few recent studies have tried to incorporate the sequential patterns of micro-behaviors into SR models. However, those sequential models still cannot effectively capture all the inherent interdependencies between micro-behavior operations. In this work, we aim to investigate the effects of the micro-behavior information in SR systematically. Specifically, we identify two different patterns of micro-behaviors: ``sequential patterns'' and ``dyadic relational patterns''. To build a unified model of user micro-behaviors, we first devise a multigraph to aggregate the sequential patterns from different items via a graph neural network, and then utilize an extended self-attention network to exploit the pair-wise relational patterns of micro-behaviors. Extensive experiments on three public real-world datasets show the superiority of the proposed approach over the state-of-the-art baselines and confirm the usefulness of these two different micro-behavior patterns for SR.


    Item Type: Book Section
    Keyword(s) / Subject(s): session-based recommendation, micro-behavior modeling, graph neural networks, self-attention mechanism
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for
    Depositing User: Dell Zhang
    Date Deposited: 07 Jul 2022 13:07
    Last Modified: 09 Aug 2023 12:53


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