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Disentangling User Intention for Sequential Recommendation with Dual Intention Decoupling Network

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Database Systems for Advanced Applications (DASFAA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13944))

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Abstract

Modern recommender systems often use sequential neural networks to capture users’ dynamic and evolving intentions from behavior data. However, a user’s different intentions might evolve over time at different speeds. Some user intentions are relatively stable with respect to time (i.e., time-invariant), and simply feeding all behavior data to sequential neural networks might not capture these time-invariant intentions well, since the inductive bias of sequential neural networks could prefer time-varying patterns than time-invariant patterns. In this paper, we propose a novel Dual Intention Decoupling Network (DIDN) framework to model time-invariant patterns and time-varying patterns in users’ behavior data separately, thus both types of patterns could be modeled more accurately. To do so, we first introduce a self-attention based model and a tree-based clustering algorithm to model time-varying and time-invariant patterns respectively, and then combine these two models to generate the overall click-through rate prediction. In the self-attention module, we further introduce a candidate item attention mechanism to implicitly decouple a user’s mixed intentions. Experimental results on three benchmarks show that our DIDN outperforms the state-of-the-art baselines in the topk sequential recommendation task.

This work was partially supported by MoE-CMCC “Artificial Intelligence” Project No. MCM20190701.

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Notes

  1. 1.

    In this paper, we focus on the intention decoupling paradigm. Thus we omit more details about the multi-head self-attention sub-layer  [15] for brevity.

  2. 2.

    After computing the multiple interest embeddings and the corresponding multiple matching scores, we use an argmax operator to choose the final matching score.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Liangliang Chen & Guang Chen

Authors
  1. Liangliang Chen
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  2. Guang Chen
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Corresponding author

Correspondence to Guang Chen .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

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Chen, L., Chen, G. (2023). Disentangling User Intention for Sequential Recommendation with Dual Intention Decoupling Network. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_3

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  • DOI: https://doi.org/10.1007/978-3-031-30672-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30671-6

  • Online ISBN: 978-3-031-30672-3

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