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

Author:

Xu Mengfei,Wang ShuORCID,Song Chenlong,Zhu Anqi,Zhu Yunqiang,Zou ZhiqiangORCID

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.

Funder

National Natural Science Foundation of China

Strategic Priority Research Program of the Chinese Academy of Sciences

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference32 articles.

1. Knowledge embedding towards the recommendation with sparse user-item interactions;Yang;Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,2019

2. Collaborative Filtering Approach: A Review of Recent Research;Kawtar;Proceedings of the 3rd International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD),2020

3. Improved DeepWalk Algorithm Based on Preference Random Walk;Ye;Proceedings of the International Conference Natural Language Processing,2019

4. Knowledge graph convolutional networks for recommender systems;Wang;Proceedings of the International Conference on World Wide Web,2019

5. A Survey on Knowledge Graph-Based Recommender Systems

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