A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering

Recently, recommender systems based on Graph Convolution Network (GCN) have become a research hotspot, especially in collaborative filtering. However, most GCN-based models have inferior embedding propagation mechanism, leading to low information extraction efficiency. Besides, the existing methods suffer from high computational complexity for large user-item interaction graphs. In order to solve the above problems, we propose LII-GCCF that integrates
L
inear transformation,
I
nitial residual and
I
dentity mapping into the
G
raph
C
onvolutional
C
ollaborative
F
iltering model. First, initial residual and identity mapping are applied to optimize the information propagation of graph convolution, which privide abundant interaction and alleviate information loss problem. Second, LII-GCCF removes the unnecessary nonlinear transformation based on the characteristics of collaborative filtering to simplify the graph convolution process. Comprehensive experiments are conducted on two public datasets, and the results demonstrate that LII-GCCF has a significant improvement over other state-of-the-art methods.