Output details
11 - Computer Science and Informatics
Goldsmiths' College
Article title
A probabilistic approach to mining mobile phone data sequences
Type
D - Journal article
Title of journal
Personal and Ubiquitous Computing
Article number
-
Volume number
n/a
Issue number
-
First page of article
n/a
ISSN of journal
16174909
Year of publication
2013
Number of additional authors
1
Additional information
<15> This paper presents a novel latent topic model developed as an extension of “Latent Dirichlet Allocation” addressing the incorporation of sequences, particularly long sequences representative of location data, into the topic model. The model has been evaluated on synthetic data as well as two types of real location data (GPS and cell tower connections). This paper was an invited journal paper, extending the nominated best conference paper at ISWC (IEEE Symposium on Wearable Computers 22% acceptance rate). Many research groups have requested the model code, including Telefonica and Southampton University. The paper provides an entirely new modelling strategy.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
