{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:38:11Z","timestamp":1781019491199,"version":"3.54.1"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Foundation of Yunnan Key Laboratory of Service Computing","award":["YNSC23103"],"award-info":[{"award-number":["YNSC23103"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"crossref","award":["ZR202212050072"],"award-info":[{"award-number":["ZR202212050072"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"name":"ARC DECRA","award":["DE210101458"],"award-info":[{"award-number":["DE210101458"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can be utilized to mine their preferences, thereby facilitating the promotion of the geological tourism industry. However, such check-ins often contain sensitive user information that poses privacy leakage risks. To address this issue, some methods have been proposed to develop privacy-preserving point-of-interest (POI) recommendation systems. These methods commonly rely on either perturbation-based or federated learning techniques to protect users\u2019 privacy. However, the former can hinder preference capture, while the latter remains vulnerable to privacy breaches during the parameter-sharing process. To overcome these challenges, we propose a novel privacy-preserving POI recommendation model that incorporates users\u2019 privacy preferences based on a simplified graph convolutional neural network. Specifically, we employ a generative model to create a subset of POIs that reflect users\u2019 preferences but do not reveal their private information, and then we design a simplified graph convolutional network to analyze the high-order connectivity between users and POIs that are privacy-preserving. The resulting model enables efficient POI recommendation under strict privacy protection, which is particularly relevant to geological tourism. Experimental results on two public datasets demonstrate the effectiveness of our proposed approach.<\/jats:p>","DOI":"10.1145\/3620677","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T12:19:56Z","timestamp":1693829996000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":78,"title":["Privacy-preserving Point-of-interest Recommendation based on Simplified Graph Convolutional Network for Geological Traveling"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4911-5744","authenticated-orcid":false,"given":"Yuwen","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3488-4679","authenticated-orcid":false,"given":"Xiaokang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, Shiga University, Japan and RIKEN Center for Advanced Intelligence Project, Hikone, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6497-3643","authenticated-orcid":false,"given":"Huaizhen","family":"Kou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7278-3097","authenticated-orcid":false,"given":"Yawu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4879-9803","authenticated-orcid":false,"given":"Xiaolong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7353-4159","authenticated-orcid":false,"given":"Xuyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7746-4901","authenticated-orcid":false,"given":"Lianyong","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao China and Yunnan Key Laboratory of Service Computing, Kunming, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3341104"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394138"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110137"},{"issue":"4","key":"e_1_3_1_5_2","first-page":"1","article-title":"LightFR: Lightweight federated recommendation with privacy-preserving matrix factorization","volume":"41","author":"Zhang Honglei","year":"2023","unstructured":"Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, and Yidong Li. 2023. 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BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).","journal-title":"arXiv preprint arXiv:1205.2618"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/445"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3620677","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3620677","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:43Z","timestamp":1750291423000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3620677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,27]]},"references-count":21,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8,31]]}},"alternative-id":["10.1145\/3620677"],"URL":"https:\/\/doi.org\/10.1145\/3620677","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,27]]},"assertion":[{"value":"2023-05-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}