Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge
Overfitting in a deep neural network leads to low recommendation precision and high loss. To mitigate these issues in a deep neural network-based recommendation algorithm, we propose a recommendation algorithm, LG-DropEdge, joint light graph convolutional network, and the DropEdge. First, to reduce...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/3843021 |
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| _version_ | 1832553702543589376 |
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| author | Haicheng Qu Jiangtao Guo Yanji Jiang |
| author_facet | Haicheng Qu Jiangtao Guo Yanji Jiang |
| author_sort | Haicheng Qu |
| collection | DOAJ |
| description | Overfitting in a deep neural network leads to low recommendation precision and high loss. To mitigate these issues in a deep neural network-based recommendation algorithm, we propose a recommendation algorithm, LG-DropEdge, joint light graph convolutional network, and the DropEdge. First, to reduce the cost of data storage and calculation, we initialize user and item embedding in the embedding layer of the algorithm. Then, to obtain high-order interaction relationships to optimize the embedding representation, we enrich the embedding by injecting high-order connectivity relationships in the convolutional layer. In the training phase, DropEdge is used to randomly discard connected relationships (interaction edges) to prevent overfitting. Finally, to reasonably aggregate the embedding results learned on all layers, the weighted average is expressed as the final embedding, so that users can make preferences in the item. We conduct experiments on three public datasets, using two performance indicators; namely, recall and NDCG, are used for evaluation. For the Gowalla dataset, compared with the optimal baseline method, recall@20 and ndcg@20 increased by 2.53% and 2.39%, respectively. For the Yelp2018 dataset, recall@20 and ndcg@20 increased by 6.17% and 5.58%, respectively. For the Amazon-book dataset, recall@20 and ndcg@20 increased by 4.82% and 4.67%, respectively. The results show that LG-DropEdge can not only reduce the degree of neural network overfitting but also improve the recommended results’ precision. |
| format | Article |
| id | doaj-art-aaf1928cf3874b539af157efce81b530 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-aaf1928cf3874b539af157efce81b5302025-02-03T05:53:27ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/3843021Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdgeHaicheng Qu0Jiangtao Guo1Yanji Jiang2College of SoftwareCollege of SoftwareCollege of SoftwareOverfitting in a deep neural network leads to low recommendation precision and high loss. To mitigate these issues in a deep neural network-based recommendation algorithm, we propose a recommendation algorithm, LG-DropEdge, joint light graph convolutional network, and the DropEdge. First, to reduce the cost of data storage and calculation, we initialize user and item embedding in the embedding layer of the algorithm. Then, to obtain high-order interaction relationships to optimize the embedding representation, we enrich the embedding by injecting high-order connectivity relationships in the convolutional layer. In the training phase, DropEdge is used to randomly discard connected relationships (interaction edges) to prevent overfitting. Finally, to reasonably aggregate the embedding results learned on all layers, the weighted average is expressed as the final embedding, so that users can make preferences in the item. We conduct experiments on three public datasets, using two performance indicators; namely, recall and NDCG, are used for evaluation. For the Gowalla dataset, compared with the optimal baseline method, recall@20 and ndcg@20 increased by 2.53% and 2.39%, respectively. For the Yelp2018 dataset, recall@20 and ndcg@20 increased by 6.17% and 5.58%, respectively. For the Amazon-book dataset, recall@20 and ndcg@20 increased by 4.82% and 4.67%, respectively. The results show that LG-DropEdge can not only reduce the degree of neural network overfitting but also improve the recommended results’ precision.http://dx.doi.org/10.1155/2022/3843021 |
| spellingShingle | Haicheng Qu Jiangtao Guo Yanji Jiang Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge Journal of Advanced Transportation |
| title | Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge |
| title_full | Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge |
| title_fullStr | Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge |
| title_full_unstemmed | Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge |
| title_short | Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge |
| title_sort | research on recommendation algorithm of joint light graph convolution network and dropedge |
| url | http://dx.doi.org/10.1155/2022/3843021 |
| work_keys_str_mv | AT haichengqu researchonrecommendationalgorithmofjointlightgraphconvolutionnetworkanddropedge AT jiangtaoguo researchonrecommendationalgorithmofjointlightgraphconvolutionnetworkanddropedge AT yanjijiang researchonrecommendationalgorithmofjointlightgraphconvolutionnetworkanddropedge |