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：图卷积网络通过利用用户-物品交互图的节点信息与拓扑结构，已成为学习用户和物品嵌入表示的关键推荐系统技术。然而，这类模型常面临著名的过度平滑问题，导致用户和物品嵌入区分度降低，削弱个性化效果。传统基于GCN的推荐系统中的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模型无关方法——基于GCN的推荐系统去平滑框架（DGR）。该方法通过全局与局部双视角，有效解决通用GCN推荐模型的过度平滑问题。具体而言，我们首先在每次消息传递层引入向量扰动，借助全局拓扑结构引导，惩罚节点嵌入过度趋同的倾向。同时，进一步为读出嵌入设计了定制化损失项，以保持用户与其邻域物品间的局部协同关系。特别地，与邻域物品具有高相关性的物品也被纳入考量，以强化局部拓扑信息。为验证方法有效性，我们在5个基准数据集上基于5个经典GCN推荐模型开展广泛实验，结果表明所提框架具有良好的有效性与泛化能力。