T-Gram: A Time-Aware Language Model to Predict Human Mobility


  • Hsun-Ping Hsieh National Taiwan University
  • Cheng-Te Li Academia Sinica
  • Xiaoqing Gao Xidian University




human mobility, language model


This paper presents a novel time-aware language model, T-gram, to predict the human mobility using location check-in data. While the conventional n-gram language model, which use the contextual co-occurrence to estimate the probability of a sequence of items, are often employed to predict human mobility, the time information of items is merely considered. T-gram exploits the time information associated at each location, and aims to estimate the probability of visiting satisfaction for a given sequence of locations. For a location sequence, if locations are visited at right times and the transitions between locations are proper as well, the T-gram probability gets higher. We also devise a T-gram Search algorithm to predict future locations. Experiments of human mobility prediction conducted on Gowalla check-in data significantly outperform a series of n-gram-based methods and encourage the future usage of T-gram.




How to Cite

Hsieh, H.-P., Li, C.-T., & Gao, X. (2021). T-Gram: A Time-Aware Language Model to Predict Human Mobility. Proceedings of the International AAAI Conference on Web and Social Media, 9(1), 614-617. https://doi.org/10.1609/icwsm.v9i1.14663