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.

翻译：图卷积网络（GCNs）通过利用用户-项目交互图的节点信息与拓扑结构，在学习用户和项目嵌入方面已成为推荐系统的关键工具。然而，此类模型常面临著名的过平滑问题，导致用户和项目嵌入区分度降低，个性化能力减弱。在基于GCN的系统中，传统去平滑方法具有模型特异性，缺乏通用解决方案。本文提出一种新颖的、与模型无关的方法——基于GCN的推荐系统去平滑框架（DGR）。该方法通过同时考虑全局与局部视角，有效解决了通用GCN推荐模型中的过平滑问题。具体而言，我们首先在每次消息传递层引入向量扰动，以全局拓扑结构为指导，惩罚节点嵌入过度趋于相似的倾向。同时，我们进一步为读出嵌入设计了一项定制损失项，用于保留用户与其相邻项目之间的局部协作关系。特别地，与相邻项目具有高相关性的项目也被纳入其中，以增强局部拓扑信息。为验证该方法，我们基于5个知名GCN推荐模型在5个基准数据集上开展了大量实验，证明了所提框架的有效性与泛化能力。