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User view dynamic graph-driven sequential recommendation

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Abstract

In most recommendation scenarios, user information is difficult to obtain due to user privacy and data protection issues. Some graph-based methods can learn the user’s feature information through the structural relationship in both user graphs and item graphs. However, a user’s latent associations with other users, especially those hidden in the user’s sequential behavior, are not well identified in the sequential recommendation. In this work, we propose a user view dynamic graph-driven sequential recommender to find out different user latent associations without additional user information. Our model can not only find out the global associations of users, but also discover the user dynamic associations through information dissemination during training. In particular, the dynamic associations are highlighted via contrastive learning to refine global associations from the user view to achieve more efficient sequential recommendations. Furthermore, our approach can serve as a container for commonly used sequential recommenders to achieve better performances. Experimental results show that the user view information has a positive guiding effect on sequential recommendation and our approach outperforms state-of-the-art models.

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Notes

  1. https://www.yelp.com/dataset

  2. https://dbis.uibk.ac.at/node/263#nowplaying

  3. https://github.com/uestcnlp/STAMP

  4. https://github.com/hidasib/GRU4Rec

  5. https://github.com/kang205/SASRec

  6. https://github.com/johnny12150/GCSAN

  7. https://github.com/CCIIPLab/GCEGNN

  8. http://jmcauley.ucsd.edu/data/amazon/

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (61902231, 62106137), the Guangdong Basic and Applied Basic Research Foundation (2023A1515011240), and the Higher Education Special Project of Guangdong Education Science Planning (2021GXJK241). We would like to thank the editors and the anonymous reviewers for their efforts.

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Authors and Affiliations

  1. Department of Computer Science, College of Engineering, Shantou University, No. 243 Daxue Road, Shantou, 515063, Guangdong, China

    Jianzhen Chen, Lin Zheng & Sentao Chen

  2. Key Laboratory of Intelligent Manufacturing Technology, Ministry of Education, Shantou University, No. 243 Daxue Road, Shantou, 515063, Guangdong, China

    Lin Zheng & Sentao Chen

Authors
  1. Jianzhen Chen
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  2. Lin Zheng
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Correspondence to Lin Zheng.

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Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Availability of data and materials

The datasets analyzed during the current study are available in the [Yelp] repository, [https://www.yelp.com/dataset], in the [DBIS] repository, [https://dbis.uibk.ac.at/node/263#nowplaying], and in the [Amazon] repository, [http://jmcauley.ucsd.edu/data/amazon/].

Code availability

We will make the PyTorch code of our implementation publicly available when the paper is published.

Authors’ contributions

Lin Zheng and Sentao Chen contributed to conceptualization and writing—review and editing; Jianzhen Chen contributed to methodology and writing—original draft preparation; Jianzhen Chen and Lin Zheng contributed to formal analysis and investigation; Lin Zheng performed funding acquisition and supervision.

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Chen, J., Zheng, L. & Chen, S. User view dynamic graph-driven sequential recommendation. Knowl Inf Syst 65, 2541–2569 (2023). https://doi.org/10.1007/s10115-023-01840-7

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