Graph neural networks (GNNs) have gained prominence as an effective technique for representation learning and have found wide application in tag recommendation tasks. Existing approaches aim to encode the hidden collaborative information among entities into embedding representations by propagating node information between connected nodes. However, in sparse observable graph structures, a significant number of connections are missing, leading to incomplete and biased propagation. To address these issues, we propose a novel model called Low-frequency Spectral Graph Convolution Networks with one-hop connections information for Personalized Tag Recommendation (LSGCNT). This model utilizes graph convolution in the spectral domain and incorporates a graph structure comprising two bipartite graphs, the user–tag interaction graph and the item–tag interaction graph. Our model aims to reduce information loss caused by propagation by utilizing graph convolution networks with trainable convolution kernels to recover preference information. In order to preserve useful low-frequency signals, we couple graph convolution with low-pass filters in the frequency domain. Through reconstructing the true rating tensor and ranking the tag scores within the tensor, we can achieve top-N recommendations. Furthermore, to preserve the one-hop connection information of the bipartite graphs, we treat the observed two bipartite graphs as two homogeneous graphs, where both users and tags contribute to the convolution of a node in the user–tag graph, and both items and tags contribute to the convolution of a node in the item–tag graph. Lastly, we analyze the impact of different internal components, pooling methods, parameter choices, and prediction approaches of LSGCNT on recommendation performance. Experimental results on two real-world datasets demonstrate that LSGCNT achieves superior recommendation performance compared with eight other state-of-the-art recommendation models.

图形神经网络 （GNN） 作为一种有效的表示学习技术而受到重视，并在标签推荐任务中得到了广泛的应用。现有方法旨在通过在连接的节点之间传播节点信息，将实体之间隐藏的协作信息编码为嵌入表示。然而，在稀疏的可观察图形结构中，大量连接缺失，导致传播不完整和有偏差。为了解决这些问题，我们提出了一种称为低频光谱图卷积网络的新模型，其中包含用于个性化标签推荐 （LSGCNT） 的单跳连接信息。该模型在谱域中利用图卷积，并包含一个由两个二分图组成的图结构，即用户-标签交互图和项目-标签交互图。我们的模型旨在通过利用带有可训练卷积核的图卷积网络来恢复偏好信息，从而减少传播造成的信息损失。为了保留有用的低频信号，我们在频域中将图卷积与低通滤波器耦合。通过重建真实的评分张量并对张量内的标签分数进行排名，我们可以实现前 N 个推荐。此外，为了保留二分图的单跳连接信息，我们将观察到的两个二分图视为两个齐次图，其中用户和标签都有助于用户标签图中节点的卷积，而项目和标签都有助于项目标签图中节点的卷积。最后，我们分析了 LSGCNT 的不同内部组件、池化方法、参数选择和预测方法对推荐性能的影响。 在两个真实数据集上的实验结果表明，与其他八个最先进的推荐模型相比，LSGCNT 实现了卓越的推荐性能。