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Volume 2 | Issue 2 | Year 2012 | Article Id. IJPTT-V2I2P403 | DOI : https://doi.org/10.14445/22492615/IJPTT-V2I2P403Mining users’ Behaviors and Environments for Semantic Place Prediction
A. Aqueel Ahmed, P. Rizwan Ahmed
Citation :
A. Aqueel Ahmed, P. Rizwan Ahmed, "Mining users’ Behaviors and Environments for Semantic Place Prediction," International Journal of P2P Network Trends and Technology (IJPTT), vol. 2, no. 2, pp. 15-21, 2012. Crossref, https://doi.org/10.14445/22492615/IJPTT-V2I2P403
Abstract
In this work, we propose a novel prediction
framework, which takes into account the spatial property, temporal property,
users’ behavior and environment at the same time, for semantic place
prediction. The core idea of our proposal is to extract features to represent
end users’ behaviors in each place related to its semantic. To achieve this
goal, we define 54 features to represent end users’ behaviors to capture the
key properties of places recorded in MDC Data Set. In our framework, we propose
a novel model, namely Multi-Level Classification Model , to solve the
imbalanced data problem. Based on the Multi-Level Classification Model, we make
semantic prediction of a place by combining several classification models. To
our best knowledge, this is the first work on predicting semantic label of
places through integrating sub-classification models into a multi-level
structure.
Keywords
Semantic Prediction, User Behavior, Feature Extraction, Multi-Level Classification Model.
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