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

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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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, , Minneapolis, MN, United States, 2010-08-20 - 2010-08-22

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