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Mining human location-routines using a multi-level approach to topic modeling

Mining human location-routines using a multi-level approach to topic modeling
Mining human location-routines using a multi-level approach to topic modeling

In this work we address the problem of modeling varying time duration sequences for large-scale human routine discovery from cellphone sensor data using a multi-level approach to probabilistic topic models. We use an unsupervised learning approach that discovers human routines of varying durations ranging from half-hourly to several hours. Our methodology can handle large sequence lengths based on a principled procedure to deal with potentially large routine-vocabulary sizes, and can be applied to rather naive initial vocabularies to discover meaningful location-routines. We successfully apply the model to a large, real-life dataset, consisting of 97 cellphone users and 16 months of their location patterns, to discover routines with varying time durations.

446-451
IEEE
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
583e99b0-abef-4d2a-b54f-70ab7b498975
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
583e99b0-abef-4d2a-b54f-70ab7b498975

Farrahi, Katayoun and Gatica-Perez, Daniel (2010) Mining human location-routines using a multi-level approach to topic modeling. In 2010 IEEE Second International Conference on Social Computing. IEEE. pp. 446-451 . (doi:10.1109/SocialCom.2010.71).

Record type: Conference or Workshop Item (Paper)

Abstract

In this work we address the problem of modeling varying time duration sequences for large-scale human routine discovery from cellphone sensor data using a multi-level approach to probabilistic topic models. We use an unsupervised learning approach that discovers human routines of varying durations ranging from half-hourly to several hours. Our methodology can handle large sequence lengths based on a principled procedure to deal with potentially large routine-vocabulary sizes, and can be applied to rather naive initial vocabularies to discover meaningful location-routines. We successfully apply the model to a large, real-life dataset, consisting of 97 cellphone users and 16 months of their location patterns, to discover routines with varying time durations.

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More information

Published date: 29 November 2010
Venue - Dates: 2nd IEEE International Conference on Social Computing, SocialCom 2010, 2nd IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2010, United States, 2010-08-19 - 2010-08-21

Identifiers

Local EPrints ID: 430700
URI: http://eprints.soton.ac.uk/id/eprint/430700
PURE UUID: 9a2bba18-eee7-4712-96e8-4a2c9c14140c

Catalogue record

Date deposited: 08 May 2019 16:30
Last modified: 07 Oct 2020 00:52

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