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Improving graph collaborative filtering with view explorer for social recommendation

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Abstract

Social recommender systems (SRS) have garnered adequate attention due to the supplementary information provided by social network, which aids in making recommendations. However, social network information contains noise, which can be detrimental to recommendation performance. Current social recommendation models are deficient in feature validation and extraction of social data. To fill that gap, we propose a novel model called Social View Explorer Collaborative Filtering (SVE-CF) which aims to extract significant consistent signals from the noisy social network. First, SVE-CF correlates users’ social and interaction behaviors, creating follow, joint, and interaction views to represent all interaction patterns. Second, it samples unlabeled examples from users to assess consistency across the three views, assigning pseudo-labels as evidence of social homophily. Third, it selects top-k pseudo-labels to amplify significant consistent signals and minimize noise through tri-view joint learning. Extensive experiments are conducted to demonstrate the effectiveness of the proposed model over the commonly used state-of-the-art (SOTA) methods.

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Data Availability

No datasets were generated or analysed during the current study.

Notes

  1. https://github.com/Nova-Lumiere/SVE-CF

  2. https://github.com/librahu/HIN-Datasets-for-Recommendation-and-Network-Embedding

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Acknowledgements

Financial support for this work has been provided by the National Natural Science Foundation of China under Projects 71771179, 72171176, and 72021002. The useful criticism of the manuscript provided by the editors and referees is also appreciated by the authors. We also thank Mingzhou Chen for his thorough examination of this work.

Funding

Financial support for this work has been provided by the National Natural Science Foundation of China under Projects 71771179, 72171176, and 72021002.

Author information

Author notes

  1. Yongrui Duan and Yijun Tu contributed equally to this work.

Authors and Affiliations

  1. School of Economics and Management, Tongji University, 1239 Si** Road, Shanghai, 200092, China

    Yongrui Duan, Yijun Tu & Yusheng Lu

  2. School of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, 9990077, Hong Kong

    Yusheng Lu

  3. Glorious Sun School of Business and Management, Donghua University, 1882 West Yan’an Road, Shanghai, 200051, China

    **aofeng Wang

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  1. Yongrui Duan
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Contributions

Yijun Tu designed the model and conducted the experiments. Yusheng Lu and Yongrui Duan, and **aofeng Wang provided the article framework and experimental opinions and revised the literature review. All authors drafted, revised and approved the manuscript.

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Correspondence to Yusheng Lu.

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Duan, Y., Tu, Y., Lu, Y. et al. Improving graph collaborative filtering with view explorer for social recommendation. J Intell Inf Syst (2024). https://doi.org/10.1007/s10844-024-00865-w

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