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Modelling heterogeneous location habits in human populations for location prediction under data sparsity

Modelling heterogeneous location habits in human populations for location prediction under data sparsity
Modelling heterogeneous location habits in human populations for location prediction under data sparsity
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 specific to individuals. To illustrate the advantages of population modelling, we apply our model to the difficult problem of overcoming data sparsity in location prediction systems, using the Nokia dataset comprising 38 individuals, and find 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.
human behavior learning, mobile phone sensing, humanactivity inference, graphical models
978-1-4503-1770-2
469-478
McInerney, James
d473987b-b706-43f5-a0a7-5e3d36fa945c
Zheng, Jiangchuan
56f2c749-b35b-4409-90a6-ef7016859d56
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
McInerney, James
d473987b-b706-43f5-a0a7-5e3d36fa945c
Zheng, Jiangchuan
56f2c749-b35b-4409-90a6-ef7016859d56
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

McInerney, James, Zheng, Jiangchuan, Rogers, Alex and Jennings, Nicholas R. (2013) Modelling heterogeneous location habits in human populations for location prediction under data sparsity. International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), Zurich, Switzerland. 08 - 12 Sep 2013. pp. 469-478 . (doi:10.1145/2493432.2493437).

Record type: Conference or Workshop Item (Paper)

Abstract

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 specific to individuals. To illustrate the advantages of population modelling, we apply our model to the difficult problem of overcoming data sparsity in location prediction systems, using the Nokia dataset comprising 38 individuals, and find 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.

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e-pub ahead of print date: 8 September 2013
Venue - Dates: International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), Zurich, Switzerland, 2013-09-08 - 2013-09-12
Keywords: human behavior learning, mobile phone sensing, humanactivity inference, graphical models
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 354656
URI: http://eprints.soton.ac.uk/id/eprint/354656
ISBN: 978-1-4503-1770-2
PURE UUID: 84358400-9ebf-466e-a2e9-3765fce48c71

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Date deposited: 29 Jul 2013 10:51
Last modified: 14 Mar 2024 14:22

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Contributors

Author: James McInerney
Author: Jiangchuan Zheng
Author: Alex Rogers
Author: Nicholas R. Jennings

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