Learning Time-Aware Distributed Representations of Locations from Spatio-Temporal Trajectories

Huaiyu Wan, Fuchen Li, Shengnan Guo, Zhong Cao, Youfang Lin

Abstract: The goal of location representation learning is to learn an embedded feature vector for each location. We propose a Time-Aware Location Embedding (TALE) method to learn distributed representations of locations from users’ spatio-temporal trajectories, in which a novel tree structure is designed to incorporate the temporal information in the hierarchical softmax model. We utilize TALE to improve two location-based prediction tasks to verify its effectiveness.