  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.
