Sharpness-Aware Graph Collaborative Filtering
Huiyuan Chen Chin-Chia Michael Yeh Yujie Fan Yan Zheng Junpeng Wang Vivian Lai Mahashweta Das Hao Yang
Graph Neural Networks (GNNs) have achieved impressive performance in
collaborative filtering. However, GNNs tend to yield inferior performance when
the distributions of training and test data are not aligned well. Also,
training GNNs requires optimizing non-convex neural networks with an abundance
of local and global minima, which may differ widely in their performance at
test time. Thus, it is essential to choose the minima carefully. Here we
propose an effective training schema, called {gSAM}, under the principle that
the \textit{flatter} minima has a better generalization ability than the
\textit{sharper} ones. To achieve this goal, gSAM regularizes the flatness of
the weight loss landscape by forming a bi-level optimization: the outer problem
conducts the standard model training while the inner problem helps the model
jump out of the sharp minima. Experimental results show the superiority of our
gSAM.