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Robust enhanced collaborative filtering without explicit noise filtering

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Abstract

Graph convolutional neural networks have been successfully applied to collaborative filtering to capture high-quality user-item representations. Despite their remarkable performance, there are still limitations that hinder further improvement of recommender systems. Most existing recommendation systems utilize implicit feedback data for model training, but such data inevitably contains adversarial interaction noise. The conventional graph-based collaborative filtering method fails to effectively filter out this noise, and instead amplifies its impact, resulting in degraded model performance. To address this issue, we propose a robustness-enhanced collaborative filtering graph neural network model that does not rely on explicit noise filtering. Our approach involves simulating user-item interactions that do not exist in practice as adversarial interaction noise using random noise. To mitigate the impact of this noise in hidden feedback, we replace them with randomly selected partial nodes based on the principle of mutual information maximization. Our model has been extensively experimented on three public datasets (MovieLens-1 M, Yelp, and Ta-feng) and achieves performance improvements of about 5%, 10%, and 14%, respectively, compared to the state-of-the-art baseline model. In particular, in model robustness experiments, our model achieves significant performance improvements of about 13% and 17% in Yelp and Ta-feng. A comprehensive experimental study shows that our proposed method is reasonably effective and interpretable.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

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

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

  3. https://www.kaggle.com/datasets/chiranjivdas09/ta-feng-grocery-dataset.

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Acknowledgements

I have received a great deal of support and assistance in the writing of this thesis. I would first like to thank my supervisor, Professor Zhenhai Wang, whose expertise was invaluable in formulating the research questions and methodology. In addition particular, I would like to thank my team members for their patient support and assistance.

Funding

This research was funded by The Key Research and Development Program of Linyi City [2022028] and The Natural Science Foundation of Shandong Province, China (Grant No. ZR2023MA027).

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Authors and Affiliations

  1. College of Information Science and Engineering, Linyi University, Linyi, 276000, Shandong, China

    Rong Fan, Zhenhai Wang, Yunlong Guo, Yuhao Xu & Zhiru Wang

  2. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Weimin Li

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  1. Rong Fan
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  2. Zhenhai Wang
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  3. Yunlong Guo
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  4. Yuhao Xu
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  5. Zhiru Wang
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Contributions

All authors contributed to the conception and design of the study. Fan Rong completed the experimental analysis and first draft of the manuscript. Review and supervision of the paper was done by Zhenhai Wang, Yuhao Xu, and Yunlong Guo. Data organization, validation work, and visualization were done by Zhiru Wang and by Weimin Li. All authors read and approved the final manuscript.

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Correspondence to Zhenhai Wang.

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Fan, R., Wang, Z., Guo, Y. et al. Robust enhanced collaborative filtering without explicit noise filtering. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06086-w

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