{"type":"ScholarlyArticle","title":"Trajectory-User Linking via Variational AutoEncoder","authors":[{"name":"Fan Zhou","slug":"fan-zhou","given":"Fan","family":"Zhou","affiliation":"School of Information and Software Engineering , University of Electronic Science and Technology of China."},{"name":"Qiang Gao","slug":"qiang-gao","given":"Qiang","family":"Gao","affiliation":"School of Information and Software Engineering , University of Electronic Science and Technology of China."},{"name":"Goce Trajcevski","slug":"goce-trajcevski","given":"Goce","family":"Trajcevski","affiliation":"Iowa State University ,"},{"name":"Kunpeng Zhang","slug":"kunpeng-zhang","given":"Kunpeng","family":"Zhang","affiliation":"University of Maryland , College"},{"name":"Ting Zhong","slug":"ting-zhong","given":"Ting","family":"Zhong","affiliation":"School of Information and Software Engineering , University of Electronic Science and Technology of China."},{"name":"Fengli Zhang","slug":"fengli-zhang","given":"Fengli","family":"Zhang","affiliation":"School of Information and Software Engineering , University of Electronic Science and Technology of China."}],"abstract":"Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification. Existing works on mining mobility patterns often model trajectories using Markov Chains (MC) or recurrent neural networks (RNN) -- either assuming independence between non-adjacent locations or following a shallow generation process. However, most of them ignore the fact that human trajectories are often sparse, high-dimensional and may contain embedded hierarchical structures. We tackle the TUL problem with a semi-supervised learning framework, called TULVAE (TUL via Variational AutoEncoder), which learns the human mobility in a neural generative architecture with stochastic latent variables that span hidden states in RNN. TULVAE alleviates the data sparsity problem by leveraging large-scale unlabeled data and represents the hierarchical and structural semantics of trajectories with high-dimensional latent variables. Our experiments demonstrate that TULVAE improves efficiency and linking performance in real GTSM datasets, in comparison to existing methods.","doi":"10.24963/ijcai.2018/446","url":"https://ijcai.org/proceedings/2018/446","pdf":"https://ijcai.org/proceedings/2018/0446.pdf","markdown":"https://ijcai.org/proceedings/2018/446.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/446","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":446,"pages":{"first":3212,"last":3218},"keywords":["Machine Learning: Time-series;Data Streams","Machine Learning: Classification","Machine Learning: Learning Generative Models"],"videos":[],"references":[{"position":1,"raw":"[Bowman et al., 2016] Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Józefowicz, and Samy Bengio. 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