{"type":"ScholarlyArticle","title":"Identifying Human Mobility via Trajectory Embeddings","authors":[{"name":"Qiang Gao","slug":"qiang-gao","given":"Qiang","family":"Gao","affiliation":"* University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Fan Zhou","slug":"fan-zhou","given":"Fan","family":"Zhou","affiliation":"* University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Kunpeng Zhang","slug":"kunpeng-zhang","given":"Kunpeng","family":"Zhang","affiliation":"‡ University of Maryland , College park"},{"name":"Goce Trajcevski","slug":"goce-trajcevski","given":"Goce","family":"Trajcevski","affiliation":"§ Northwestern University , Evanston ,"},{"name":"Xucheng Luo","slug":"xucheng-luo","given":"Xucheng","family":"Luo","affiliation":"* University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Fengli Zhang","slug":"fengli-zhang","given":"Fengli","family":"Zhang","affiliation":"* University of Electronic Science and Technology of China , Chengdu , China"}],"abstract":"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.","doi":"10.24963/ijcai.2017/234","url":"https://ijcai.org/proceedings/2017/234","pdf":"https://ijcai.org/proceedings/2017/0234.pdf","markdown":"https://ijcai.org/proceedings/2017/234.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/234","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":234,"pages":{"first":1689,"last":1695},"keywords":["Machine Learning: Classification","Machine Learning: Semi-Supervised Learning","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Alharbi et al., 2016] Basma Alharbi, Abdulhakim Qahtan, and Xiangliang Zhang. 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