{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T08:39:00Z","timestamp":1720168740558},"reference-count":25,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2016,11,1]],"date-time":"2016-11-01T00:00:00Z","timestamp":1477958400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2016,11]]},"abstract":"<jats:p> Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people\u2019s future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user\u2019s social relations in real world and variances in trajectory and then utilize this association to model the user\u2019s mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers\u2019 trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity\u2013based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity\u2013based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches. <\/jats:p>","DOI":"10.1177\/1550147716678426","type":"journal-article","created":{"date-parts":[[2016,11,26]],"date-time":"2016-11-26T02:05:21Z","timestamp":1480125921000},"page":"155014771667842","update-policy":"http:\/\/dx.doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A novel trajectory similarity\u2013based approach for location prediction"],"prefix":"10.1177","volume":"12","author":[{"given":"Zelei","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"given":"Liang","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"given":"Chunyi","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"given":"Yan","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, China"}]},{"given":"Jia","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Changchun University of Technology, Changchun, China"},{"name":"High-Assurance Software Laboratory, INESC TEC & University of Minho, Braga, Portugal"}]}],"member":"179","published-online":{"date-parts":[[2016,11,25]]},"reference":[{"key":"bibr1-1550147716678426","first-page":"1038","volume-title":"Proceedings of the 2012 IEEE 12th international conference on data mining, ICDM \u201912","author":"Noulas A"},{"key":"bibr2-1550147716678426","first-page":"101","volume-title":"Proceedings of sixth international symposium on wearable 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