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Recommendation Algorithm Based on Deep Light Graph Convolution Network in Knowledge Graph

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13980))

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Abstract

Recently, recommendation algorithms based on Graph Convolution Network (GCN) have achieved many surprising results thanks to the ability of GCN to learn more efficient node embeddings. However, although GCN shows powerful feature extraction capability in user-item bipartite graphs, the GCN-based methods appear powerless for knowledge graph (KG) with complex structures and rich information. In addition, all of the existing GCN-based recommendation systems suffer from the over-smoothing problem, which results in the models not being able to utilize higher-order neighborhood information, and thus these models always achieve their best performance at shallower layers. In this paper, we propose a Deep Light Graph Convolution Network for Knowledge Graph (KDL-GCN) to alleviate the above limitations. Firstly, the User-Entity Bipartite Graph approach (UE-BP) is proposed to simplify knowledge graph, which leverages entity information by constructing multiple interaction graphs. Secondly, a Deep Light Graph Convolution Network (DLGCN) is designed to make full use of higher-order neighborhood information. Finally, experiments on three real-world datasets show that the KDL-GCN proposed in this paper achieves substantial improvement compared to the state-of-the-art methods.

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Acknowledgements

This work was supported by the Basic and Applied Basic Research of Guangdong Province under grant [No.2015A030308018], the authors express their thanks to the grant.

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, GuangZhou, China

    Xiaobin Chen & Nanfeng Xiao

Authors
  1. Xiaobin Chen
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  2. Nanfeng Xiao
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Correspondence to Nanfeng Xiao .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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Chen, X., Xiao, N. (2023). Recommendation Algorithm Based on Deep Light Graph Convolution Network in Knowledge Graph. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_14

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  • DOI: https://doi.org/10.1007/978-3-031-28244-7_14

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  • Online ISBN: 978-3-031-28244-7

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