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Adaptive discriminant feature learning for GNN-based session recommendation

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Abstract

Session-based recommendation(SBR) utilizes anonymous sessions to predict the next interaction without considering user profiles. The existing works generally tend to improve SBR performance by learning complex item transitions and enriching session representations. However, the item features obtained by mining the higher-order information do not integrate the contribution of lower-order information, which can easily lead to the over-smoothing problem. Additionally, as a component of session representations, the local interest is typically represented by the last-clicked item of a session, which may deviate from the user’s main intent when the last-clicked one is noise. To solve the abovementioned problems, we propose a novel method named Adaptive Discriminant Feature Learning (ADFL) based on graph neural networks for SBR. The proposed method involves two newly designed components: a paralleled gating module (PGM) and a feature-enhancing unit (FEU). In particular, PGM integrates all orders of item information through the nested application of gating mechanisms, which can overcome the over-smoothing problem and enhance item transitions. Meanwhile, the user’s main intent obtained based on the entire session is introduced to learn the local interest. FEU utilizes the user’s main intent to learn more discriminative and reasonable local interest, easing the effect of the last-clicked item being noise in some sessions. Extensive experiments on three real-world datasets show that the proposed method outperforms the comparative methods. Codes and data are available at https://github.com/St-ding/ADFL.

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Data availability

No datasets were generated or analysed during the current study.

Notes

  1. https://tianchi.aliyun.com/dataset/dataDetail?dataId=42.

  2. http://dbis-nowplaying.uibk.ac.at/nowplaying.

  3. http://cikm2016.cs.iupui.edu/cikm-cup/.

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Acknowledgements

The authors thank the reviewers for their helpful and insightful comments.

Funding

This work was supported by Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No.KYCX22_0950), Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications (Grant No.NY223030), and Nanjing Science and Technology Innovation Foundation for Overseas Students (Grants No.RK002NLX23004).

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Authors and Affiliations

  1. School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, New Model Road, Nanjing, 210003, Jiangsu, China

    Jiawei Ding, Zhiyi Tan, Guanming Lu & Jinsheng Wei

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  1. Jiawei Ding
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Contributions

Jiawei Ding conducted the experiment and wrote the initial manuscript. Zhiyi Tan and Jinsheng Wei reviewed and edited it. Guanming Lu edited it and performed the statistical analysis.

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Correspondence to Zhiyi Tan.

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The authors declare that they have no competing financial or personal interests that could have influenced this work. The authors declare no conflict of interest.

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Communicated by Junyu Gao.

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Ding, J., Tan, Z., Lu, G. et al. Adaptive discriminant feature learning for GNN-based session recommendation. Multimedia Systems 31, 44 (2025). https://doi.org/10.1007/s00530-024-01644-x

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