FourierKAN-GCF: Fourier Kolmogorov-Arnold Network -- An Effective and Efficient Feature Transformation for Graph Collaborative Filtering
<|reference_start|>FourierKAN-GCF: Fourier Kolmogorov-Arnold Network -- An Effective and Efficient Feature Transformation for Graph Collaborative Filtering: Graph Collaborative Filtering (GCF) has achieved state-of-the-art performance for recommendation tasks. However, most GCF structures simplify the feature transformation and nonlinear operation during message passing in the graph convolution network (GCN). We revisit these two components and discover that a part of feature transformation and nonlinear operation during message passing in GCN can improve the representation of GCF, but increase the difficulty of training.   In this work, we propose a simple and effective graph-based recommendation model called FourierKAN-GCF. Specifically, it utilizes a novel Fourier Kolmogorov-Arnold Network (KAN) to replace the multilayer perceptron (MLP) as a part of the feature transformation during message passing in GCN, which improves the representation power of GCF and is easy to train. We further employ message dropout and node dropout strategies to improve the representation power and robustness of the model. Extensive experiments on two public datasets demonstrate the superiority of FourierKAN-GCF over most state-of-the-art methods. The implementation code is available at https://github.com/Jinfeng-Xu/FKAN-GCF.<|reference_end|>
@article{xu2024fourierkan-gcf:,
    title={FourierKAN-GCF: Fourier Kolmogorov-Arnold Network -- An Effective and
  Efficient Feature Transformation for Graph Collaborative Filtering},
    author={Jinfeng Xu, Zheyu Chen, Jinze Li, Shuo Yang, Wei Wang, Xiping Hu,
  Edith C.-H. Ngai},
    journal={arXiv preprint arXiv:2406.01034},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01034},
    primaryClass={cs.IR}
}