Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preference edges, which significantly reduces the performance of recommendations. In this paper, we study how to enhance the graph structure for CF more effectively, thereby optimizing the representation of graph nodes. Previous works introduced matrix completion techniques into CF, proposing the use of either stochastic completion methods or superficial structure completion to address this issue. However, most of these approaches employ random numerical filling that lack control over noise perturbations and limit the in-depth exploration of higher-order interaction features of nodes, resulting in biased graph representations. In this paper, we propose an Amplify Graph Learning framework based on Sparsity Completion (called AGL-SC). First, we utilize graph neural network to mine direct interaction features between user and item nodes, which are used as the inputs of the encoder. Second, we design a factorization-based method to mine higher-order interaction features. These features serve as perturbation factors in the latent space of the hidden layer to facilitate generative enhancement. Finally, by employing the variational inference, the above multi-order features are integrated to implement the completion and enhancement of missing graph structures. We conducted benchmark and strategy experiments on four real-world datasets related to recommendation tasks. The experimental results demonstrate that AGL-SC significantly outperforms the state-of-the-art methods.

翻译：图学习模型已在基于协同过滤（CF）的推荐系统中得到广泛应用。由于数据稀疏性问题，原始输入的图结构缺乏潜在的正向偏好边，这显著降低了推荐性能。本文研究如何更有效地增强用于CF的图结构，从而优化图节点的表示。先前的研究将矩阵补全技术引入CF，提出了使用随机补全方法或浅层结构补全来解决此问题。然而，这些方法大多采用随机数值填充，缺乏对噪声扰动的控制，并限制了对节点高阶交互特征的深入挖掘，导致图表示存在偏差。本文提出了一种基于稀疏性补全的增强图学习框架（称为AGL-SC）。首先，我们利用图神经网络挖掘用户与物品节点间的直接交互特征，并将其作为编码器的输入。其次，我们设计了一种基于分解的方法来挖掘高阶交互特征。这些特征在隐藏层的潜在空间中作为扰动因子，以促进生成式增强。最后，通过采用变分推断，将上述多阶特征进行整合，实现对缺失图结构的补全与增强。我们在四个与推荐任务相关的真实数据集上进行了基准测试与策略实验。实验结果表明，AGL-SC显著优于现有最先进方法。