T-gram: A time-aware language model to predict human mobility

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel time-aware language model, Tgram, 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 Tgram 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.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Web and Social Media, ICWSM 2015
PublisherAAAI Press
Pages614-617
Number of pages4
ISBN (Electronic)9781577357339
Publication statusPublished - 2015 Jan 1
Event9th International Conference on Web and Social Media, ICWSM 2015 - Oxford, United Kingdom
Duration: 2015 May 262015 May 29

Publication series

NameProceedings of the 9th International Conference on Web and Social Media, ICWSM 2015

Other

Other9th International Conference on Web and Social Media, ICWSM 2015
CountryUnited Kingdom
CityOxford
Period15-05-2615-05-29

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'T-gram: A time-aware language model to predict human mobility'. Together they form a unique fingerprint.

  • Cite this

    Hsieh, H-P., Li, C-T., & Gao, X. (2015). T-gram: A time-aware language model to predict human mobility. In Proceedings of the 9th International Conference on Web and Social Media, ICWSM 2015 (pp. 614-617). (Proceedings of the 9th International Conference on Web and Social Media, ICWSM 2015). AAAI Press.