NDLI: Modeling temporal effects of human mobile behavior on location-based social networks

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Modeling temporal effects of human mobile behavior on location-based social networks

Content Provider	ACM Digital Library
Author	Gao, Huiji Hu, Xia Tang, Jiliang Liu, Huan
Abstract	The rapid growth of location-based social networks (LBSNs) invigorates an increasing number of LBSN users, providing an unprecedented opportunity to study human mobile behavior from spatial, temporal, and social aspects. Among these aspects, temporal effects offer an essential contextual cue for inferring a user's movement. Strong temporal cyclic patterns have been observed in user movement in LBSNs with their correlated spatial and social effects (i.e., temporal correlations). It is a propitious time to model these temporal effects (patterns and correlations) on a user's mobile behavior. In this paper, we present the first comprehensive study of temporal effects on LBSNs. We propose a general framework to exploit and model temporal cyclic patterns and their relationships with spatial and social data. The experimental results on two real-world LBSN datasets validate the power of temporal effects in capturing user mobile behavior, and demonstrate the ability of our framework to select the most effective location prediction algorithm under various combinations of prediction models.
Starting Page	1673
Ending Page	1678
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505616
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Temporal effect Human mobile behavior Location prediction Location-based social networks
Content Type	Text
Resource Type	Article

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