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Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services.<\/jats:p>","DOI":"10.1007\/s40747-022-00917-0","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T14:03:06Z","timestamp":1670248986000},"page":"3277-3300","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9757-1915","authenticated-orcid":false,"given":"Chonghuan","family":"Xu","sequence":"first","affiliation":[]},{"given":"Xinyao","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Dongsheng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Kaidi","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Austin Shijun","family":"Ding","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"issue":"3","key":"917_CR1","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.ipm.2018.02.005","volume":"54","author":"C Xu","year":"2018","unstructured":"Xu C (2018) A novel recommendation method based on social network using matrix factorization technique. 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