  Graph Convolutional Networks (GCNs) have become pivotal in recommendation
systems for learning user and item embeddings by leveraging the user-item
interaction graph's node information and topology. However, these models often
face the famous over-smoothing issue, leading to indistinct user and item
embeddings and reduced personalization. Traditional desmoothing methods in
GCN-based systems are model-specific, lacking a universal solution. This paper
introduces a novel, model-agnostic approach named \textbf{D}esmoothing
Framework for \textbf{G}CN-based \textbf{R}ecommendation Systems
(\textbf{DGR}). It effectively addresses over-smoothing on general GCN-based
recommendation models by considering both global and local perspectives.
Specifically, we first introduce vector perturbations during each message
passing layer to penalize the tendency of node embeddings approximating overly
to be similar with the guidance of the global topological structure. Meanwhile,
we further develop a tailored-design loss term for the readout embeddings to
preserve the local collaborative relations between users and their neighboring
items. In particular, items that exhibit a high correlation with neighboring
items are also incorporated to enhance the local topological information. To
validate our approach, we conduct extensive experiments on 5 benchmark datasets
based on 5 well-known GCN-based recommendation models, demonstrating the
effectiveness and generalization of our proposed framework.
