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SCIENTIA SINICA Informationis, Volume 46, Issue 9: 1288-1297(2016) https://doi.org/10.1360/N112015-00237

Mining mobility patterns based on temporal and sequential features

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  • ReceivedNov 10, 2015
  • AcceptedDec 28, 2015
  • PublishedSep 9, 2016

Abstract

The wide-spread use of positioning devices (e.g., GPS) has given rise to a mass of spatio-temporal trajectories, which enable us to mine user-mobility patterns. In this paper, we proposed a model based on sequential and temporal trajectory features to mine people's latent movement patterns. Considering the following trajectory characteristics---(1) location sequences play a pivotal role in understanding user-mobility patterns, and (2) user-mobility patterns change over time---we first modeled the location sequences and then incorporated the temporal information into the model. To verify the effectiveness of our model, we performed thorough empirical studies on a check-in dataset of the Gowalla social network. The experimental results confirmed that the proposed method performed better than Latent Dirichlet Allocation (LDA) and T-BiLDA.


Funded by

国家自然科学基金(61272092)

国家自然科学基金(61572289)

山东省自然科学基金(ZR2012FZ004)

山东省自然科学基金(ZR2015FM002)

山东省科技发展计划基金(2014GGE27178)

国家重点基础研究发展计划(973计划)

(2015CB352500)


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