Enhanced Sub-graph Reconstruction Graph Neural Network for Recommendation
Personalized recommendation can recommend items of interest to different users and is widely used in the real world. Among them, graph collaborative filtering is a method of personalized recommendation. It can enrich the connection between users and items on the basis of collaborative filtering, to...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2355425 |
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| _version_ | 1850116201871572992 |
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| author | Zhe Liu Xiaojun Lou Jian Li Guanjun Liu |
| author_facet | Zhe Liu Xiaojun Lou Jian Li Guanjun Liu |
| author_sort | Zhe Liu |
| collection | DOAJ |
| description | Personalized recommendation can recommend items of interest to different users and is widely used in the real world. Among them, graph collaborative filtering is a method of personalized recommendation. It can enrich the connection between users and items on the basis of collaborative filtering, to learn the embedded representation of nodes more accurately. Since graph collaborative filtering is based on bipartite graphs, few exciting graph collaborative methods consider the relationships between users (or items), the message between homogeneous nodes are diluted or ignored. Predicting and constructing the relationship between users (or items) has become a challenging. To solve this problem, we propose an enhanced sub-graph reconstruction graph neural network for recommendation (SRCF), using a heterogeneous graph neural network based encoder-decoder learn potential relationships between users (or items), and reconstruct sub-graphs based on those relationships. In the proposed model, the information of user and item sub-graphs is merged with the network of graph collaborative filtering, which enhances effective information transfer between homogeneous nodes, thereby improving the model performance. We have selected a number of data sets of different scenarios and different scales to comprehensively evaluate the performance of the model, and the experimental results confirmed the superiority of our model. |
| format | Article |
| id | doaj-art-6d5aa9ec9a334506b0d47182dcbbed98 |
| institution | OA Journals |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-6d5aa9ec9a334506b0d47182dcbbed982025-08-20T02:36:22ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2355425Enhanced Sub-graph Reconstruction Graph Neural Network for RecommendationZhe Liu0Xiaojun Lou1Jian Li2Guanjun Liu3College of Media Engineering, Communication University of Zhejiang, Hangzhou, ChinaSchool of Mathematics and Computer science, Zhejiang A and F University, Hangzhou, ChinaSchool of Mathematics and Computer science, Zhejiang A and F University, Hangzhou, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai, ChinaPersonalized recommendation can recommend items of interest to different users and is widely used in the real world. Among them, graph collaborative filtering is a method of personalized recommendation. It can enrich the connection between users and items on the basis of collaborative filtering, to learn the embedded representation of nodes more accurately. Since graph collaborative filtering is based on bipartite graphs, few exciting graph collaborative methods consider the relationships between users (or items), the message between homogeneous nodes are diluted or ignored. Predicting and constructing the relationship between users (or items) has become a challenging. To solve this problem, we propose an enhanced sub-graph reconstruction graph neural network for recommendation (SRCF), using a heterogeneous graph neural network based encoder-decoder learn potential relationships between users (or items), and reconstruct sub-graphs based on those relationships. In the proposed model, the information of user and item sub-graphs is merged with the network of graph collaborative filtering, which enhances effective information transfer between homogeneous nodes, thereby improving the model performance. We have selected a number of data sets of different scenarios and different scales to comprehensively evaluate the performance of the model, and the experimental results confirmed the superiority of our model.https://www.tandfonline.com/doi/10.1080/08839514.2024.2355425 |
| spellingShingle | Zhe Liu Xiaojun Lou Jian Li Guanjun Liu Enhanced Sub-graph Reconstruction Graph Neural Network for Recommendation Applied Artificial Intelligence |
| title | Enhanced Sub-graph Reconstruction Graph Neural Network for Recommendation |
| title_full | Enhanced Sub-graph Reconstruction Graph Neural Network for Recommendation |
| title_fullStr | Enhanced Sub-graph Reconstruction Graph Neural Network for Recommendation |
| title_full_unstemmed | Enhanced Sub-graph Reconstruction Graph Neural Network for Recommendation |
| title_short | Enhanced Sub-graph Reconstruction Graph Neural Network for Recommendation |
| title_sort | enhanced sub graph reconstruction graph neural network for recommendation |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2355425 |
| work_keys_str_mv | AT zheliu enhancedsubgraphreconstructiongraphneuralnetworkforrecommendation AT xiaojunlou enhancedsubgraphreconstructiongraphneuralnetworkforrecommendation AT jianli enhancedsubgraphreconstructiongraphneuralnetworkforrecommendation AT guanjunliu enhancedsubgraphreconstructiongraphneuralnetworkforrecommendation |