Mining Human Location-Routines Using a Multi-Level Approach to Topic Modeling
| Type of publication: | Conference paper |
| Citation: | Farrahi_SOCIALCOM-2_2010 |
| Booktitle: | 2010 IEEE Second International Conference on Social Computing, SIN Symposium |
| Year: | 2010 |
| Month: | 8 |
| Location: | Minneapolis, Minnesota, USA |
| 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. |
| Keywords: | |
| Projects: |
Idiap SNSF-MULTI |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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