Katayoun Farrahi, Daniel Gatica-Perez,
"Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model"
: International Symposium on Wearable Computers (ISWC), 6-2012
Original Titel:
Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model
Sprache des Titels:
Englisch
Original Buchtitel:
International Symposium on Wearable Computers (ISWC)
Original Kurzfassung:
Mining patterns of human behavior from large-scale mobile phone
data has
potential to understand certain phenomena in society.
The study of such human-centric massive datasets
requires new
mathematical models. In this paper, we propose a probabilistic
topic model that we call the distant n-gram topic model
(DNTM)
to address the problem of learning long duration
human location
sequences. The DNTM is based on Latent Dirichlet Allocation
(LDA).
We define the generative process for the model, derive
the inference procedure
and evaluate our model on real mobile
data. We consider two different real-life human
datasets,
collected by mobile phone locations, the first considering GPS
locations
and the second considering cell tower connections. The
DNTM successfully
discovers topics on the two datasets.
Finally, the DNTM is
compared to LDA by considering
log-likelihood performance on unseen data,
showing the
predictive power of the model on unseen data. We
find that the
DNTM consistantly outperforms LDA as the sequence length
increases.