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Multi-Self-Supervised Light Graph Convolution Network for Social Recommendation

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Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14179))

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Abstract

Self-supervised learning is a novel paradigm between unsupervised and supervised learning, which can use the ground-truth samples automatically generated from the raw data for self-identification based contrastive learning and effectively alleviate the problem of data sparsity. Its ingenious combination with graph convolution network is a major breakthrough in the current recommendation field. However, mostly existing methods fail to fully consider the interference factors in social relations, fail to effectively improve the deviation of predicting scores only relying on the user and item representations, and fail to take into account the time cost of model training while improving the recommendation performance. To address these issues, this paper proposes a social recommendation framework based on multi-self-supervised light graph convolution network, named as MSR. Technically, MSR first utilizes the user-item sampling interaction graph to enhance the learning of user/item interaction representations, and then further enhances the learning of user social representations by fusing social relations through two-stage encoding. Finally, the interaction representations, social representations, and item bias are combined to predict scores. Our experiments on two real-world datasets LastFM and Douban-Book demonstrate that, compared with the current mainstream models, MSR improves the Precision@10 at least 2.35% and 10.41%, respectively, and reduces the time consumption at least 35.35% and 76.48%, respectively.

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Notes

  1. 1.

    http://files.grouplens.org/datasets/hetrec 2011/.

  2. 2.

    https://github.com/librahu/HIN-Datasets-for-Recommendation-and-NetworkEmbedding.

  3. 3.

    https://matpool.com/host-market/cpu.

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Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Hubei University, Wuhan, 430062, China

    Yunsheng Zhou, Yinying Zhou & Dunhui Yu

  2. Education Informationization Engineering and Technology Center of Hubei Province, Wuhan, 430000, China

    Dunhui Yu

Authors
  1. Yunsheng Zhou
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  2. Yinying Zhou
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  3. Dunhui Yu
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Corresponding author

Correspondence to Dunhui Yu .

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

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Zhou, Y., Zhou, Y., Yu, D. (2023). Multi-Self-Supervised Light Graph Convolution Network for Social Recommendation. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_25

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  • DOI: https://doi.org/10.1007/978-3-031-46674-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46673-1

  • Online ISBN: 978-3-031-46674-8

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