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 prop...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01643-5 |
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| _version_ | 1832571184959455232 |
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| author | Zhengshun Fei Haotian Zhou Jinglong Wang Gui Chen Xinjian Xiang |
| author_facet | Zhengshun Fei Haotian Zhou Jinglong Wang Gui Chen Xinjian Xiang |
| author_sort | Zhengshun Fei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-98d226a54105472ba9299ffb57a7f5fb |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-98d226a54105472ba9299ffb57a7f5fb2025-02-02T12:48:45ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111112210.1007/s40747-024-01643-5Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendationZhengshun Fei0Haotian Zhou1Jinglong Wang2Gui Chen3Xinjian Xiang4School of Automation and Electrical Engineering, Zhejiang University of Science and TechnologySchool of Automation and Electrical Engineering, Zhejiang University of Science and TechnologySchool of Automation and Electrical Engineering, Zhejiang University of Science and TechnologyBingwu (Ningbo) Intelligent Equipment Co., LTDSchool of Automation and Electrical Engineering, Zhejiang University of Science and TechnologyAbstract 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.https://doi.org/10.1007/s40747-024-01643-5Recommendation systemsPersonalized tag recommendationGraph convolutional networkRepresentation learning |
| spellingShingle | Zhengshun Fei Haotian Zhou Jinglong Wang Gui Chen Xinjian Xiang Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation Complex & Intelligent Systems Recommendation systems Personalized tag recommendation Graph convolutional network Representation learning |
| title | Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation |
| title_full | Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation |
| title_fullStr | Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation |
| title_full_unstemmed | Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation |
| title_short | Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation |
| title_sort | low frequency spectral graph convolution networks with one hop connections information for personalized tag recommendation |
| topic | Recommendation systems Personalized tag recommendation Graph convolutional network Representation learning |
| url | https://doi.org/10.1007/s40747-024-01643-5 |
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