{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T22:38:47Z","timestamp":1792190327730,"version":"4.4.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2013,12,1]],"date-time":"2013-12-01T00:00:00Z","timestamp":1385856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001868","name":"National Science Council Taiwan","doi-asserted-by":"publisher","award":["NSC101-2221-E-006-255-MY3 and NSC100-2631-H-006-001"],"award-info":[{"award-number":["NSC101-2221-E-006-255-MY3 and NSC100-2631-H-006-001"]}],"id":[{"id":"10.13039\/501100001868","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2013,12]]},"abstract":"<jats:p>\n                    In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of location recommendation, that is, they predict the next location of a user using location recommenders. However, an user usually visits somewhere for reasons other than interestingness. In this article, we propose a novel mining-based location prediction approach called\n                    <jats:italic>Geographic-Temporal-Semantic-based Location Prediction (GTS-LP),<\/jats:italic>\n                    which takes into account a user's geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user in visiting a location. The core idea underlying our proposal is the discovery of trajectory patterns of users, namely\n                    <jats:italic>GTS patterns<\/jats:italic>\n                    , to capture frequent movements triggered by the three kinds of intentions. To achieve this goal, we define a new trajectory pattern to capture the key properties of the behaviors that are motivated by the three kinds of intentions from trajectories of users. In our\n                    <jats:italic>GTS-LP<\/jats:italic>\n                    approach, we propose a series of novel matching strategies to calculate the similarity between the current movement of a user and discovered GTS patterns based on various moving intentions. On the basis of similitude, we make an online prediction as to the location the user intends to visit. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that explores the geographic, temporal, and semantic properties simultaneously. By means of a comprehensive evaluation using various real trajectory datasets, we show that our proposed\n                    <jats:italic>GTS-LP<\/jats:italic>\n                    approach delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.\n                  <\/jats:p>","DOI":"10.1145\/2542182.2542184","type":"journal-article","created":{"date-parts":[[2014,1,2]],"date-time":"2014-01-02T08:09:43Z","timestamp":1388650183000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":84,"title":["Mining geographic-temporal-semantic patterns in trajectories for location prediction"],"prefix":"10.1145","volume":"5","author":[{"given":"Josh Jia-Ching","family":"Ying","sequence":"first","affiliation":[{"name":"National Cheng Kung University, Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang-Chien","family":"Lee","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, University Park, PA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent S.","family":"Tseng","sequence":"additional","affiliation":[{"name":"National Cheng Kung University, Taiwan, ROC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,1,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Towards semantic trajectory knowledge discovery. 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