IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation
In this era of information overload, to better provide personalized content services to users, recommendation systems have greatly improved the efficiency of information distribution. Graph Convolution Network(GCN), which is one of the representative works of graph structure aggregation processing,...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10105951/ |
| _version_ | 1797833182043701248 |
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| author | Jingxue Zhang Changchun Yang |
| author_facet | Jingxue Zhang Changchun Yang |
| author_sort | Jingxue Zhang |
| collection | DOAJ |
| description | In this era of information overload, to better provide personalized content services to users, recommendation systems have greatly improved the efficiency of information distribution. Graph Convolution Network(GCN), which is one of the representative works of graph structure aggregation processing, works by node convolution with the help of the Laplacian matrix of the graph and weighted combination of neighbor node information according to the outgoing and incoming degrees of neighbor nodes to obtain the representation of the current node. However, the mainstream GCN models nowadays do not take into account data augmentation of metadata and the fact that each node plays different roles with different importance and weights, thus making the recommendation performance limited. To better solve the above problems, we propose the IcaGCN model, which can perform data augmentation and calculate node weights in modules, and is a convenient plug-and-play method. Finally, extensive experimental results on four real-world datasets have shown the effectiveness and robustness of the proposed model. Especially on the Amazon-Book dataset, our IcaGCN has improved by 6.32%, 42.29%, and 12.38% in Recall@20, MRR@20, and NDCG@20, respectively, compared to other existing state-of-the-art models. We also provide source code and data to reproduce the experimental results available at <uri>https://github.com/PersonZ1223/IcaGCN.git</uri> |
| first_indexed | 2024-04-09T14:20:25Z |
| format | Article |
| id | doaj.art-39581d27209c4dffab7e05dd3cbbd975 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T14:20:25Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-39581d27209c4dffab7e05dd3cbbd9752023-05-04T23:00:26ZengIEEEIEEE Access2169-35362023-01-0111418484185810.1109/ACCESS.2023.326861610105951IcaGCN: Model Intents via Coactivated Graph Convolution Network for RecommendationJingxue Zhang0https://orcid.org/0000-0003-3236-2543Changchun Yang1https://orcid.org/0000-0001-9567-630XSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, ChinaSchool of Microelectronics and Control Engineering, Changzhou University, Changzhou, ChinaIn this era of information overload, to better provide personalized content services to users, recommendation systems have greatly improved the efficiency of information distribution. Graph Convolution Network(GCN), which is one of the representative works of graph structure aggregation processing, works by node convolution with the help of the Laplacian matrix of the graph and weighted combination of neighbor node information according to the outgoing and incoming degrees of neighbor nodes to obtain the representation of the current node. However, the mainstream GCN models nowadays do not take into account data augmentation of metadata and the fact that each node plays different roles with different importance and weights, thus making the recommendation performance limited. To better solve the above problems, we propose the IcaGCN model, which can perform data augmentation and calculate node weights in modules, and is a convenient plug-and-play method. Finally, extensive experimental results on four real-world datasets have shown the effectiveness and robustness of the proposed model. Especially on the Amazon-Book dataset, our IcaGCN has improved by 6.32%, 42.29%, and 12.38% in Recall@20, MRR@20, and NDCG@20, respectively, compared to other existing state-of-the-art models. We also provide source code and data to reproduce the experimental results available at <uri>https://github.com/PersonZ1223/IcaGCN.git</uri>https://ieeexplore.ieee.org/document/10105951/Recommender systemsgraph neural networkscollaborative filtering |
| spellingShingle | Jingxue Zhang Changchun Yang IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation IEEE Access Recommender systems graph neural networks collaborative filtering |
| title | IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation |
| title_full | IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation |
| title_fullStr | IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation |
| title_full_unstemmed | IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation |
| title_short | IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation |
| title_sort | icagcn model intents via coactivated graph convolution network for recommendation |
| topic | Recommender systems graph neural networks collaborative filtering |
| url | https://ieeexplore.ieee.org/document/10105951/ |
| work_keys_str_mv | AT jingxuezhang icagcnmodelintentsviacoactivatedgraphconvolutionnetworkforrecommendation AT changchunyang icagcnmodelintentsviacoactivatedgraphconvolutionnetworkforrecommendation |