Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation

Abstract

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.