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The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users\u2019 check\u2010in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable\u2010order Markov model to determine the distribution of a user\u2019s POIs based on his or her travel histories in LBSNs. To further enhance the user\u2019s experience, we also apply linear discriminant analysis to cluster the topics related to <jats:italic>\u201cTravel\u201d<\/jats:italic> and connect to users with social links or similar interests. The probability of POIs based on users\u2019 historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative\u2010filtering\u2010based and other methods using two real\u2010world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.<\/jats:p>","DOI":"10.1155\/2019\/8503962","type":"journal-article","created":{"date-parts":[[2019,2,12]],"date-time":"2019-02-12T23:45:41Z","timestamp":1550015141000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Discovering Travel Community for POI Recommendation on Location\u2010Based Social Networks"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5465-610X","authenticated-orcid":false,"given":"Lei","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9170-2766","authenticated-orcid":false,"given":"Dandan","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9920-7751","authenticated-orcid":false,"given":"Zongtao","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchi","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,2,12]]},"reference":[{"key":"e_1_2_11_1_2","unstructured":"ChengC. 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