McInerney, James, Zheng, Jiangchuan, Rogers, Alex and Jennings, Nicholas R.
Modelling heterogeneous location habits in human populations for location prediction under data sparsity
At International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), Switzerland.
08 - 12 Sep 2013.
10 pp, .
In recent years, researchers have sought to capture the daily life location behaviour of groups of people for exploratory, inference, and predictive purposes. However, development of such approaches has been limited by the requirement of personal semantic labels for locations or social/spatial overlap between individuals in the group. To address this shortcoming, we present a Bayesian model of mobility in populations (i.e., groups without spatial or social interconnections) that is not subject to any of these requirements. The model intelligently shares temporal parameters between people, but keeps the spatial parameters speci?c to individuals. To illustrate the advantages of population modelling, we apply our model to the dif?cult problem of overcoming data sparsity in location prediction systems, using the Nokia dataset comprising 38 individuals, and ?nd a factor of 2.4 improvement in location prediction performance against a state-of-the-art model when training on only 20 hours of observations.
Conference or Workshop Item
|Digital Object Identifier (DOI):
|Venue - Dates:
||International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), Switzerland, 2013-09-08 - 2013-09-12
||human behavior learning, mobile phone sensing, human
activity inference, graphical models
||Agents, Interactions & Complexity
|8 September 2013||e-pub ahead of print|
||29 Jul 2013 10:51
||17 Apr 2017 15:14
|Further Information:||Google Scholar|
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