Trajectory-User Linking via Variational AutoEncoder

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.

Authors

• Fan Zhou University of Electronic Science and Technology of China
• Qiang Gao School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China Engineering Research Center of Intelligent Finance, Ministry of Education, Chengdu, China
• Goce Trajcevski Iowa State University, Iowa, USA
• Kunpeng Zhang University of Maryland, College Park
• Ting Zhong University of Electronic Science and Technology of China
• Fengli Zhang

Keywords

• Machine Learning: Classification
• Machine Learning: Learning Generative Models
• Machine Learning: Time-series; Data Streams