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Enhancing Sequential Recommendation via Decoupled Knowledge Graphs

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The Semantic Web (ESWC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13261))

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Abstract

Sequential recommendation can capture dynamic interest patterns of users based on user interaction sequences. Recently, there has been interest in integrating the knowledge graph (KG) into sequential recommendation. Existing works suffer from two main challenges: a) representing each entity in the KG as a single vector can confound heterogeneous information about the entity; b) triple-based facts are modeled independently, lacking the exploration of high-order connectivity between entities. To solve the above challenges, we decouple the KG into two subgraphs, namely CRoss-user Behavior-based graph and Intrinsic Attribute-based graph (Crbia), depending on the type of relation between entities. We further propose a CrbiaNet based on the two subgraphs. First, CrbiaNet obtains behavior-level and attribute-level semantic features from these two subgraphs independently by different graph neural networks, respectively. Then, CrbiaNet applies a sequential model incorporating these semantic features to capture dynamic preference of the users. Extensive experiments on three real-world datasets show that our proposed CrbiaNet outperforms previous state-of-the-art knowledge-enhanced sequential recommendation models by a large margin consistently.

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Notes

  1. 1.

    The codes are released at https://github.com/paulpig/sequentialRec.git..

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Acknowledgement

This research project was supported by the Foundation of Science and Technology Project of Hebei Education Department (Grants No. ZD2021063).

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Authors and Affiliations

  1. Collaborative Innovation Center, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Bingchao Wu, Chenglong Deng, Bei Guan & Yongji Wang

  2. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Yongji Wang & Yuxuan Kangyang

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Bingchao Wu, Chenglong Deng, Bei Guan, Yongji Wang & Yuxuan Kangyang

Authors
  1. Bingchao Wu
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  2. Chenglong Deng
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  3. Bei Guan
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  4. Yongji Wang
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  5. Yuxuan Kangyang
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Corresponding author

Correspondence to Yongji Wang .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Universidad Simón Bolívar, Leibniz Information Centre for Science and Technology, Hannover, Niedersachsen, Germany

    Maria-Esther Vidal

  3. Institut Polytechnique de Paris "DIG", Télécom ParisTech, Palaiseau, France

    Fabian Suchanek

  4. University of Southern California, Marina del Rey, CA, USA

    Pedro Szekley

  5. IBM Research - Thomas J. Watson Research, Yorktown Heights, NY, USA

    Pavan Kapanipathi

  6. LaSIGE, Fac de Ciencias,Edif C6, Pis0 3, Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

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Wu, B., Deng, C., Guan, B., Wang, Y., Kangyang, Y. (2022). Enhancing Sequential Recommendation via Decoupled Knowledge Graphs. In: Groth, P., et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_1

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  • DOI: https://doi.org/10.1007/978-3-031-06981-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06980-2

  • Online ISBN: 978-3-031-06981-9

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