Human mobility prediction based on a hierarchical interest model

Wei Liu, Yozo Shoji, Ryoichi Shinkuma

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

Abstract

We propose a scheme to predict human mobility in this paper. First, a hierarchical interest model is introduced to organize the semantic category of locations in human mobility logs as well as representing their personalized mobility patterns. Then, by combining the interest models of different people, a 3-modes tensor with the features of person identity, time, and the semantic category of location is constructed. Tensor factorization is utilized to reveal people's mobility interest on different kinds of locations. Finally, personalized interest models are recovered from cumulative tensor to predict human mobility in a person-by-person way. Extensive evaluation results based on a large scale check-in dataset from real location-based social networks have validated that our proposal achieves better recall, precision, and F-Score in human mobility prediction as compared with the state-of-art approach.

Original languageEnglish
Title of host publicationProceedings - 20th International Symposium on Wireless Personal Multimedia Communications, WPMC 2017
PublisherIEEE Computer Society
Pages422-427
Number of pages6
ISBN (Electronic)9781538627679
DOIs
Publication statusPublished - 2018 Feb 22
Externally publishedYes
Event20th International Symposium on Wireless Personal Multimedia Communications, WPMC 2017 - Bali, Indonesia
Duration: 2017 Dec 172017 Dec 20

Publication series

NameInternational Symposium on Wireless Personal Multimedia Communications, WPMC
Volume2017-December
ISSN (Print)1347-6890

Conference

Conference20th International Symposium on Wireless Personal Multimedia Communications, WPMC 2017
Country/TerritoryIndonesia
CityBali
Period17/12/1717/12/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction

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