A Bayesian-based approach for activity and mobility inference in location-based social networks

Wen Yuan Zhu*, Yu Wen Wang, Chin Jie Chen, Wen-Chih Peng, Po Ruey Lei

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

With the popularity of location-based social networks (LBSNs), users would like to share their check-ins with their friends for more social interactions. These check-in records reflect not only when and where they are, but also what they are doing. If we can capture the relations of the location, time, and activity factors in LBSNs, the location-based social platforms can provide more personalized location-based services to users. In this paper, we aim to infer individual activity and mobility based on their check-in records in LBSNs. For these two inference problems, we analyze check-in records, and utilize Bayesian network to represent the relations among location, time, and activity factors of check-in records. Based on the proposed network model, the two inference problems can be simplified to two modules, the activity-time and the location-activity relation. For the activity-time relation, we propose Order-1 Activity Transition Model to capture the activity-time relations of check-in records. Moreover, for the location-activity relation, we exploit the Gaussian mixture model to capture individual mobility features in different activities. To evaluate the proposed network model for the two inference problems, we conduct extensive experiments on two real datasets, and the experimental results show that our proposed Bayesian-based approach has higher performance than the state-of-the-art approaches for activity and mobility inference in LBSNs.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 17th International Conference on Mobile Data Management, IEEE MDM 2016
EditorsPrem Jayaraman, Wei Wu, Chi-Yin Chow
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages152-157
Number of pages6
ISBN (Electronic)9781509008834
DOIs
StatePublished - 20 Jul 2016
Event17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016 - Porto, Portugal
Duration: 13 Jun 201616 Jun 2016

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2016-July
ISSN (Print)1551-6245

Conference

Conference17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016
CountryPortugal
CityPorto
Period13/06/1616/06/16

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