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Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering
7 references·3 citing·34 second hop
Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering (2013)Effective routine behavior pattern discov…An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data (2012)An unsupervised framework for…Predicting human behaviour from selected mobile phone data points (2010)Predicting human behaviour fr…Eigenbehaviors: identifying structure in routine (2009)Eigenbehaviors: identifying s…Eigenbehaviors: identifying structure in routine (2009)Eigenbehaviors: identifying s…What did you do today? (2008)What did you do today?Periodic properties of user mobility and access-point popularity (2006)Periodic properties of user m…Reality mining: sensing complex social systems (2005)Reality mining: sensing compl…Urban path travel time estimation using GPS trajectories from high-sampling-rate ridesourcing services (2022)Urban path travel time estima…Urban Mobility Sensing Analysis through a Layered Sensing Approach (2015)Urban Mobility Sensing Analys…Effective Mobile Context Pattern Discovery via Adapted Hierarchical Dirichlet Processes (2014)Effective Mobile Context Patt…Civil conflicts are associated with the global climate (2011)We know how you live (2013)Open data policies, their implementation and impact: A framework for comparison (2014)On the Characterization of Vehicular Mobility (2017)Profiling the Mobility of Tourists Exploring Social Sensing (2019)Time Series Analysis: Forecasting and Control (1977)Mining behavioral groups in large wireless LANs (2007)Detecting pattern changes in individual travel behavior: A Bayesian approach (2018)Predestination: Inferring Destinations from Partial Trajectories (2006)A next location prediction method for smartphones using blockmodels (2013)Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals (2017)Inferring friendship network structure by using mobile phone data (2009)Modelling heterogeneous location habits in human populations for location prediction under data sparsity (2013)Early Warning of Human Crowds Based on Query Data from Baidu Maps: Analysis Based on Shanghai Stampede (2017)Deep ROI-Based Modeling for Urban Human Mobility Prediction (2018)What's In A Name? Malay Seals As Onomastic Sources (2018)Using GPS to learn significant locations and predict movement across multiple users (2003)Learning and inferring transportation routines (2007)Social signal processing: Survey of an emerging domain (2008)Limits of Predictability in Human Mobility (2010)The Massachusetts Institute of Technology (1999)Mining large-scale smartphone data for personality studies (2011)Measuring Large-Scale Social Networks with High Resolution (2014)Mining time-dependent attractive areas and movement patterns from taxi trajectory data (2009)Using Bus Probe Data for Analysis of Travel Time Variability (2009)Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle (2015)Dynamic origin–destination flow estimation for urban road network solely using probe vehicle trajectory data (2023)Identifying subway commuters travel patterns using traffic smart card data: A topic model (2025)By their apps you shall understand them (2010)Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data (2010)Mining smartphone data for app usage prediction and recommendations: A survey (2017)Bridging the gap between physical location and online social networks (2010)Predictability of population displacement after the 2010 Haiti earthquake (2012)A survey of results on mobile phone datasets analysis (2015)
this work references cited by second hop

References

  • 2005Reality mining: sensing complex social systems2,771 cited
  • 2009Eigenbehaviors: identifying structure in routine504 cited
  • 2009Eigenbehaviors: identifying structure in routine439 cited
  • 2008What did you do today?120 cited
  • 2006Periodic properties of user mobility and access-point popularity84 cited
  • 2012An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data64 cited
  • 2010Predicting human behaviour from selected mobile phone data points29 cited

Cited by

  • 2022Urban path travel time estimation using GPS trajectories from high-sampling-rate ridesourcing services15 cited
  • 2014Effective Mobile Context Pattern Discovery via Adapted Hierarchical Dirichlet Processes13 cited
  • 2015Urban Mobility Sensing Analysis through a Layered Sensing Approach10 cited

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