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The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation

文献类型：期刊论文

作者Xu, Mengfei2,3; Wang, Shu1; Song, Chenlong2,3; Zhu, Anqi2,3; Zhu, Yunqiang1; Zou, Zhiqiang2,3
刊名APPLIED SCIENCES-BASEL
出版日期2022-08-01
卷号12期号:16页码:15
关键词recommendation system data argumentation Third Law of Geography generative adversarial network rural ecological civilization pattern
DOI10.3390/app12168024
通讯作者Wang, Shu(wangshu@igsnrr.ac.cn) ; Zou, Zhiqiang(zouzq@njupt.edu.cn)
英文摘要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.
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100100] ; Chinese Scholarship Council[202008320044] ; National Natural Science Foundation of China[42050101]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:000846366500001
出版者MDPI
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Chinese Scholarship Council ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/182355]  
专题中国科学院地理科学与资源研究所
通讯作者Wang, Shu; Zou, Zhiqiang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Big Data Secur & Intelligent Proc, Nanjing 210023, Peoples R China
3.Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210023, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Mengfei,Wang, Shu,Song, Chenlong,et al. The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation[J]. APPLIED SCIENCES-BASEL,2022,12(16):15.
APA Xu, Mengfei,Wang, Shu,Song, Chenlong,Zhu, Anqi,Zhu, Yunqiang,&Zou, Zhiqiang.(2022).The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation.APPLIED SCIENCES-BASEL,12(16),15.
MLA Xu, Mengfei,et al."The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation".APPLIED SCIENCES-BASEL 12.16(2022):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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