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Disentangled Contrastive Learning for Knowledge-Aware Recommender System

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The Semantic Web – ISWC 2023 (ISWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14265))

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Abstract

Knowledge Graphs (KGs) play an increasingly important role as useful side information in recommender systems. Recently, developing end-to-end models based on graph neural networks (GNNs) becomes the technical trend of knowledge-aware recommendation. However, we argue that prior methods are insufficient to discover multi-faceted user preferences based on diverse aspects of item attributes, since they only learn a single representation for each user and item. To alleviate this limitation, we focus on exploring user preferences from multiple aspects of item attributes, and propose a novel disentangled contrastive learning framework for knowledge-aware recommendation (DCLKR). Technically, we first disentangle item knowledge graph into multiple aspects for the knowledge view, and user-item interaction graph for the collaborative view, equipped with attentive neighbor assignment and embedding propagation mechanisms. Then we perform intra-view contrastive learning to encourage differences among disentangled representations in each view, and inter-view contrastive learning to transfer knowledge between the two views. Extensive experiments conducted on three benchmark datasets demonstrate the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/Jill5/DCLKR..

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Notes

  1. 1.

    http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

  2. 2.

    https://grouplens.org/datasets/movielens/1m/.

  3. 3.

    https://grouplens.org/datasets/hetrec-2011/.

  4. 4.

    https://searchengineland.com/library/bing/bing-satori.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their helpful comments. This work was supported by the National Nature Science Foundation of China (NSFC 61572537), and the CCF-Huawei Populus Grove Challenge Fund (CCF-HuaweiDBC202305).

Author information

Authors and Affiliations

  1. Sun Yat-Sen University, Guangzhou, China

    Shuhua Huang, Chenhao Hu, Weiyang Kong & Yubao Liu

  2. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China

    Yubao Liu

Authors
  1. Shuhua Huang
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  2. Chenhao Hu
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  4. Yubao Liu
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Corresponding author

Correspondence to Yubao Liu .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Terry R. Payne

  2. University of Bologna, Bologna, Italy

    Valentina Presutti

  3. Southeast University, Nanjing, China

    Guilin Qi

  4. Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón

  5. Huawei Technologies R&D UK, Edinburgh, UK

    Giorgos Stoilos

  6. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  7. IT University of Copenhagen, Copenhagen, Denmark

    Zoi Kaoudi

  8. Nanjing University, Nanjing, China

    Gong Cheng

  9. Tsinghua University, Beijing, Beijing, China

    Juanzi Li

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Huang, S., Hu, C., Kong, W., Liu, Y. (2023). Disentangled Contrastive Learning for Knowledge-Aware Recommender System. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14265. Springer, Cham. https://doi.org/10.1007/978-3-031-47240-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-47240-4_8

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  • Online ISBN: 978-3-031-47240-4

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