摘要：

协同过滤推荐通常面临用户-项目交互数据稀疏的挑战, 社交推荐引入用户社交关系来缓解数据稀疏性问题。多数基于图神经网络(GNN)的社交推荐系统在消息传递过程中无法根据用户偏好聚合高阶邻居信息, 造成嵌入表示过平滑和噪声问题。针对上述问题, 提出一种基于偏好感知的去噪图卷积网络的社交推荐模型PD-GCN。使用无监督学习将具有相似偏好的用户分配到用户-项目交互子图和社交子图, 在子图中进行更高阶的图卷积运算, 缓解了现有模型的过平滑问题。从全局和局部的角度出发, 通过考虑相同偏好用户节点的特征相似度和邻域节点偏好分布多样性识别并去除噪声节点, 增强模型对用户-项目交互和社交关系噪声的鲁棒性。在LastFM、Ciao、Yelp 3个公共数据集上的实验结果表明, PD-GCN模型在召回率和归一化折损累计增益两个指标上相较于其他主流模型表现出更优的性能, 验证了PD-GCN模型的有效性。

关键词: 社交推荐, 图卷积网络, 过平滑, 用户偏好, 推荐系统

Abstract:

The issue of sparsity in user-item interaction data is commonly encountered in the generation of collaborative filtering recommendations. To address this issue, social recommendations introduce users' social relationships. However, several social recommendation systems based on Graph Neural Network (GNN) cannot aggregate high-order neighbor information according to user preferences during the message-passing process, which leads to embedding oversmoothing and noise. To solve these problems, this study proposes a social recommendation model based on preference-aware denoising Graph Convolution Network(GCN), known as PD-GCN. This model employs unsupervised learning to allocate users with similar preferences to social and user-item interaction subgraphs. Higher-order graph convolution operations are performed within these subgraphs to mitigate the oversmoothing problems observed in existing models. Considering global and local perspectives, the model identifies and removes noisy nodes by weighing the feature similarity among nodes with the same preferences and diversity of neighborhood node preference distributions, thereby enhancing the robustness of the model against noise in user-item interaction and social relationships. Experimental results on three public datasets (LastFM, Ciao, and Yelp) show that the proposed model performs better than other mainstream models in terms of Recall and Normalized Discounted Cumulative Gain (NDCG), verifying the effectiveness of the PD-GCN model.

Key words: social recommendation, Graph Convolution Network(GCN), oversmoothing, user preference, recommendation system