Unveiling optimal SDG pathways: an innovative automated recommendation approach integrating graph pruning, intent graph, and attention mechanism
文献类型：期刊论文
| 作者 | Wang, Qiang1,2; Yu, Zhihang3,4; Wang, Shu1,2,5; Zhu, Yunqiang1,2,5; Dai, Xiaoliang1,2; Zou, Zhiqiang3,4; Huang, Weiming6; Claramunt, Christophe2,7 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2025-12-31 |
| 卷号 | 18期号:1页码:2513048 |
| 关键词 | Sustainable development pathways ecological civilization pattern recommender system knowledge graph pruning intent graph |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2025.2513048 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The recommendation of Sustainable Development Pathways (SDPs) is crucial for achieving the United Nations Sustainable Development Goals (SDGs) at regional level. However, traditional recommendation algorithms struggle with two key challenges: spatial heterogeneity and sparse historical interaction records between regions and SDPs. To address these issues, we introduce the Regional Graph-Based Explainable Recommendation (RGB-ER) method. RGB-ER leverages a pruned Regional Graph (RG) to capture regional spatial heterogeneity, incorporating environmental, economic, and social factors into the recommendations. In addition, an Intent Graph models regional preferences across various attributes, bridging historical interactions with the RG and mitigating data sparsity. This dual approach significantly improves recommendation accuracy and interpretability. Extensive experiments show that RGB-ER outperforms state-of-the-art graph-based models, with a maximum improvement of 9.61% in Top-3 recommendation accuracy. A case study in Fujian Province - a region characterized by its mountainous terrain, complex socio-economic landscape, and significant sustainability challenges - illustrates RGB-ER's practical applicability, aligning well with local government strategies for sustainable development. Furthermore, we assess SDPs at the county level across China, highlighting the method's potential for guiding region-specific sustainable development planning. In conclusion, RGB-ER provides a robust, explainable framework for data-driven decision-making in sustainable development. |
| URL标识 | 查看原文 |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001502973800001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214563]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Shu |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China; 3.Nanjing Univ Posts & Telecommun, Coll Comp, Nanjing, Peoples R China; 4.Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Big Data Secur & Intelligent Proc, Nanjing, Peoples R China; 5.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China; 6.Univ Leeds, Sch Geog, Leeds, England; 7.Naval Acad Res Inst, Lanveoc, France |
| 推荐引用方式 GB/T 7714 | Wang, Qiang,Yu, Zhihang,Wang, Shu,et al. Unveiling optimal SDG pathways: an innovative automated recommendation approach integrating graph pruning, intent graph, and attention mechanism[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2025,18(1):2513048. |
| APA | Wang, Qiang.,Yu, Zhihang.,Wang, Shu.,Zhu, Yunqiang.,Dai, Xiaoliang.,...&Claramunt, Christophe.(2025).Unveiling optimal SDG pathways: an innovative automated recommendation approach integrating graph pruning, intent graph, and attention mechanism.INTERNATIONAL JOURNAL OF DIGITAL EARTH,18(1),2513048. |
| MLA | Wang, Qiang,et al."Unveiling optimal SDG pathways: an innovative automated recommendation approach integrating graph pruning, intent graph, and attention mechanism".INTERNATIONAL JOURNAL OF DIGITAL EARTH 18.1(2025):2513048. |
入库方式： OAI收割
来源：地理科学与资源研究所
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