Predicting interactions and contexts with context trees
Article No.: 46, Pages 1 - 4
Abstract
Predicting the future actions of individuals from geospatial data has the potential to provide a basis for tailored services. This work presents the Predictive Context Tree (PCT), a new hierarchical classifier based on the Context Tree summary model [8]. The PCT is capable of predicting the future contexts and locations of individuals to provide a basis for understanding not only where a user will be, but also what type of activity they will be performing. Through a comparison to established techniques, this paper demonstrates the applicability of the PCT by showing increased accuracies for location prediction, and increased utility through context prediction.
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Index Terms
- Predicting interactions and contexts with context trees
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Published In
October 2016
649 pages
ISBN:9781450345897
DOI:10.1145/2996913
- General Chairs:
- Mohamed Ali,
- Shawn Newsam,
- Program Chairs:
- Matthias Renz,
- Goce Trajcevski,
- Siva Ravada
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 31 October 2016
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- Short-paper
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- EPSRC
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SIGSPATIAL'16
SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
October 31 - November 3, 2016
California, Burlingame
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SIGSPACIAL '16 Paper Acceptance Rate 40 of 216 submissions, 19%;
Overall Acceptance Rate 220 of 1,116 submissions, 20%
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