{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T22:41:36Z","timestamp":1773528096320,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Understanding human trajectory patterns is an important task in many location based social networks (LBSNs) applications, such as personalized recommendation and preference-based route planning. Most of the existing methods classify a trajectory (or its segments) based on spatio-temporal values and activities, into some predefined categories, e.g., walking or jogging. We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL). Solving the TUL problem is not a trivial task because: (1) the number of the classes (i.e., users) is much larger than the number of motion patterns in the common trajectory classification problems; and (2) the location based trajectory data, especially the check-ins, are often extremely sparse. To address these challenges, a Recurrent Neural Networks (RNN) based semi-supervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns. Experiments conducted on real-world datasets demonstrate that TULER achieves better accuracy than the existing methods.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/234","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"1689-1695","source":"Crossref","is-referenced-by-count":157,"title":["Identifying Human Mobility via Trajectory Embeddings"],"prefix":"10.24963","author":[{"given":"Qiang","family":"Gao","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China"}]},{"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}]},{"given":"Kunpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Maryland, College park"}]},{"given":"Goce","family":"Trajcevski","sequence":"additional","affiliation":[{"name":"Northwestern University, Evanston"}]},{"given":"Xucheng","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}]},{"given":"Fengli","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:52:55Z","timestamp":1501228375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/234"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/234","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}