Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network
Currently, most recommendation algorithms only use a single type of user behavior information to predict the target behavior. However, when browsing and selecting items, users generate other types of behavior information, which is important, but often not analyzed or modeled by traditional recommend...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/9/2106 |
| _version_ | 1797602802536546304 |
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| author | Huitong Lu Xiaolong Deng Junwen Lu |
| author_facet | Huitong Lu Xiaolong Deng Junwen Lu |
| author_sort | Huitong Lu |
| collection | DOAJ |
| description | Currently, most recommendation algorithms only use a single type of user behavior information to predict the target behavior. However, when browsing and selecting items, users generate other types of behavior information, which is important, but often not analyzed or modeled by traditional recommendation algorithms. This study aims to design a multi-behavior recommendation algorithm based on graph neural networks by analyzing multiple types of behavior information in users’ product purchasing process, to fully utilize multiple types of user behavior information. The algorithm models users, items, and user behavior in multiple dimensions by incorporating attention mechanisms and multi-behavior learning into graph neural networks, and solves the problem of imbalanced user behavior weights from the perspective of multi-task loss optimization. After experimental verification, we proposed that the multi-behavior graph attention network (MGAT) algorithm has better performance compared to four other classical recommendation algorithms on the Beibei and Taobao datasets. The results demonstrate that the multi-behavior recommendation algorithm based on graph neural networks has practicality in fully utilizing multiple types of user information, and can solve the problem of imbalanced user behavior weights to some extent. |
| first_indexed | 2024-03-11T04:20:40Z |
| format | Article |
| id | doaj.art-e5b3bf5adda54724ac341ad655c8e5fa |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T04:20:40Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e5b3bf5adda54724ac341ad655c8e5fa2023-11-17T22:48:45ZengMDPI AGElectronics2079-92922023-05-01129210610.3390/electronics12092106Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural NetworkHuitong Lu0Xiaolong Deng1Junwen Lu2School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaCurrently, most recommendation algorithms only use a single type of user behavior information to predict the target behavior. However, when browsing and selecting items, users generate other types of behavior information, which is important, but often not analyzed or modeled by traditional recommendation algorithms. This study aims to design a multi-behavior recommendation algorithm based on graph neural networks by analyzing multiple types of behavior information in users’ product purchasing process, to fully utilize multiple types of user behavior information. The algorithm models users, items, and user behavior in multiple dimensions by incorporating attention mechanisms and multi-behavior learning into graph neural networks, and solves the problem of imbalanced user behavior weights from the perspective of multi-task loss optimization. After experimental verification, we proposed that the multi-behavior graph attention network (MGAT) algorithm has better performance compared to four other classical recommendation algorithms on the Beibei and Taobao datasets. The results demonstrate that the multi-behavior recommendation algorithm based on graph neural networks has practicality in fully utilizing multiple types of user information, and can solve the problem of imbalanced user behavior weights to some extent.https://www.mdpi.com/2079-9292/12/9/2106graph neural networkrecommended systemmultitasking learningdata mining |
| spellingShingle | Huitong Lu Xiaolong Deng Junwen Lu Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network Electronics graph neural network recommended system multitasking learning data mining |
| title | Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network |
| title_full | Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network |
| title_fullStr | Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network |
| title_full_unstemmed | Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network |
| title_short | Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network |
| title_sort | research on efficient multi behavior recommendation method fused with graph neural network |
| topic | graph neural network recommended system multitasking learning data mining |
| url | https://www.mdpi.com/2079-9292/12/9/2106 |
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