The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation
For any rural area, a suitable ecological civilization model is of great significance and must be recommended taking into account its natural, social, and cultural characteristics so that the model is conducive to the sustainable development of its economy, environment, and industrial structure. How...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8024 |
| _version_ | 1797432473664094208 |
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| author | Mengfei Xu Shu Wang Chenlong Song Anqi Zhu Yunqiang Zhu Zhiqiang Zou |
| author_facet | Mengfei Xu Shu Wang Chenlong Song Anqi Zhu Yunqiang Zhu Zhiqiang Zou |
| author_sort | Mengfei Xu |
| collection | DOAJ |
| description | For any rural area, a suitable ecological civilization model is of great significance and must be recommended taking into account its natural, social, and cultural characteristics so that the model is conducive to the sustainable development of its economy, environment, and industrial structure. However, the rural attribute data required for such a recommendation are often missing, and the data sparsity leads to the low accuracy of and poor training effect issues in recommendation algorithms. To address this issue, this paper proposes a geographic data augmentation method, namely the spatial factor on generative adversarial networks (S-GANs), which combines the generative adversarial network (GAN) with the Third Law of Geography. Specifically, the GAN is used to generate data for the rural ecological civilization recommender system, while the Third Law of Geography is used to ensure that the generated data conform to the real geographical environment. To test the effectiveness of the S-GAN method, the experiment used the enhanced rural attribute data as the input of three recommendation systems: RippleNet, KGCN, and KGNN-LS. Compared with the data before argumentation, the recommendation accuracy increased by 55.49%, 25.12%, and 27.14% in RippleNet, KGCN, and KGNN-LS, respectively. The experimental results show that the S-GAN is effective in geographic data argumentation for recommendation and is expected to be widely used in other geographic data argumentation fields. |
| first_indexed | 2024-03-09T10:02:08Z |
| format | Article |
| id | doaj.art-099fdc43ac8a417b808e4d0669c777cf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T10:02:08Z |
| publishDate | 2022-08-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-099fdc43ac8a417b808e4d0669c777cf2023-12-01T23:20:51ZengMDPI AGApplied Sciences2076-34172022-08-011216802410.3390/app12168024The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data ArgumentationMengfei Xu0Shu Wang1Chenlong Song2Anqi Zhu3Yunqiang Zhu4Zhiqiang Zou5School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaInstitute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaInstitute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaFor any rural area, a suitable ecological civilization model is of great significance and must be recommended taking into account its natural, social, and cultural characteristics so that the model is conducive to the sustainable development of its economy, environment, and industrial structure. However, the rural attribute data required for such a recommendation are often missing, and the data sparsity leads to the low accuracy of and poor training effect issues in recommendation algorithms. To address this issue, this paper proposes a geographic data augmentation method, namely the spatial factor on generative adversarial networks (S-GANs), which combines the generative adversarial network (GAN) with the Third Law of Geography. Specifically, the GAN is used to generate data for the rural ecological civilization recommender system, while the Third Law of Geography is used to ensure that the generated data conform to the real geographical environment. To test the effectiveness of the S-GAN method, the experiment used the enhanced rural attribute data as the input of three recommendation systems: RippleNet, KGCN, and KGNN-LS. Compared with the data before argumentation, the recommendation accuracy increased by 55.49%, 25.12%, and 27.14% in RippleNet, KGCN, and KGNN-LS, respectively. The experimental results show that the S-GAN is effective in geographic data argumentation for recommendation and is expected to be widely used in other geographic data argumentation fields.https://www.mdpi.com/2076-3417/12/16/8024recommendation systemdata argumentationThird Law of Geographygenerative adversarial networkrural ecological civilization pattern |
| spellingShingle | Mengfei Xu Shu Wang Chenlong Song Anqi Zhu Yunqiang Zhu Zhiqiang Zou The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation Applied Sciences recommendation system data argumentation Third Law of Geography generative adversarial network rural ecological civilization pattern |
| title | The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation |
| title_full | The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation |
| title_fullStr | The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation |
| title_full_unstemmed | The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation |
| title_short | The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation |
| title_sort | recommendation of the rural ecological civilization pattern based on geographic data argumentation |
| topic | recommendation system data argumentation Third Law of Geography generative adversarial network rural ecological civilization pattern |
| url | https://www.mdpi.com/2076-3417/12/16/8024 |
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