###### Abstract  
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. recommendation system; data argumentation; Third Law of Geography; generative adversarial network; rural ecological civilization pattern +
Footnote †: journal: Applied  
effective auxiliary information to enrich the representation between entities [5; 6]. Relevant studies have shown that, compared with that of traditional recommendation systems, the performance of knowledge-graph-based recommendation systems has improved to some extent in some application scenarios [4; 7; 8]. However, unlike the case of recommending movies, music, news, etc., the performance of such systems is still not satisfactory when it comes to recommending rural ecological civilization patterns. This is because many rural areas lack relevant statistical data, such as the cultivated area, the road network density, and forest coverage, which cannot be supplemented by knowledge graphs, leading to the \"cold start\" problem. Therefore, generative adversarial networks (GANs) [9] are introduced to enhance the original dataset and improve the performance of the recommendation system. A GAN is based on the idea of game theory and confrontation and learns data distribution, which does not rely on any prior assumptions and generates samples in a relatively simple way [10; 11; 12]. However, unlike image data argumentation, geographic data argumentation needs to follow the Third Law of Geography [13], i.e., the more similar the geographical environment, the closer the geographical features, and so a GAN cannot be directly used to enhance rural geographic data to cope with the problem of a cold start. In the application scenarios studied in this paper, this similarity is mainly reflected in the villages with similar geographical locations. The geographical environment characteristics are similar, and the ecological civilization models are similar. Related works are described in detail in Section 2. In this paper, we investigate the problem of recommending rural ecological civilization patterns. Our design objective is to solve the problems of sparsity and a cold start in geographic data. Inspired by the GAN and the Third Law of Geography, we propose a method for recommending rural ecological civilization patterns based on geographic data argumentation. Its core idea is to analyze the digital features of each geographic element of the sample under the same pattern and generate objective geographic data based on these features; and then, these data and the original data are sent to the recommender system.