{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T22:27:34Z","timestamp":1781735254574,"version":"3.54.5"},"reference-count":25,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Science","award":["XDA23100100"],"award-info":[{"award-number":["XDA23100100"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Science","award":["202008320044"],"award-info":[{"award-number":["202008320044"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Science","award":["42050101"],"award-info":[{"award-number":["42050101"]}]},{"name":"Chinese Scholarship Council","award":["XDA23100100"],"award-info":[{"award-number":["XDA23100100"]}]},{"name":"Chinese Scholarship Council","award":["202008320044"],"award-info":[{"award-number":["202008320044"]}]},{"name":"Chinese Scholarship Council","award":["42050101"],"award-info":[{"award-number":["42050101"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA23100100"],"award-info":[{"award-number":["XDA23100100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202008320044"],"award-info":[{"award-number":["202008320044"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42050101"],"award-info":[{"award-number":["42050101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and \u2018cold start\u2019, since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the \u2018cold start\u2019 problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method.<\/jats:p>","DOI":"10.3390\/ijgi11120625","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T02:54:02Z","timestamp":1671159242000},"page":"625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features"],"prefix":"10.3390","volume":"11","author":[{"given":"Xuhui","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5355-2015","authenticated-orcid":false,"given":"Shu","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunqiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengfei","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2828-8491","authenticated-orcid":false,"given":"Zhiqiang","family":"Zou","sequence":"additional","affiliation":[{"name":"College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Trans. 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