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Modeling the Temporal Nature of Human Behavior for Demographics Prediction
9 references·4 citing·45 second hop
Modeling the Temporal Nature of Human Behavior for Demographics Prediction (2017)Modeling the Temporal Nature of Human Beh…Improving official statistics in emerging markets using machine learning and mobile phone data (2017)Improving official statistics…bandicoot: a python toolbox for mobile phone metadata (2016)bandicoot: a python toolbox f…Inferring user demographics and social strategies in mobile social networks (2014)Inferring user demographics a…BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits (2014)BayesOpt: A Bayesian Optimiza…A study of age and gender seen through mobile phone usage patterns in Mexico (2014)A study of age and gender see…Predicting Personality Using Novel Mobile Phone-Based Metrics (2013)Predicting Personality Using …Practical Bayesian Optimization of Machine Learning Algorithms (2012)Practical Bayesian Optimizati…Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti (2011)Improved Response to Disaster…A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records (2010)A Gender-Centric Analysis of …Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach (2021)Theory for socio-demographic …Link-centric analysis of variation by demographics in mobile phone communication patterns (2020)Link-centric analysis of vari…Computational socioeconomics (2019)Computational socioeconomicsEstimating individual employment status using mobile phone network data (2016)Estimating individual employm…An Introduction to the Five‐Factor Model and Its Applications (1992)The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. (1999)Understanding individual human mobility patterns (2008)Smartphone sensing methods for studying behavior in everyday life (2017)Predicting personality from patterns of behavior collected with smartphones (2020)Measuring the information society (1989)Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake (2010)Predictability of population displacement after the 2010 Haiti earthquake (2012)Applications of artificial intelligence for disaster management (2020)Social physics (2022)Economic Action and Social Structure: The Problem of Embeddedness (1985)ArnetMiner (2008)Limits of Predictability in Human Mobility (2010)Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment (2015)You Are Where You Go (2015)Sex differences in social focus across the life cycle in humans (2016)Birds of a Feather: Homophily in Social Networks (2001)Predicting Spending Behavior Using Socio-mobile Features (2013)Predicting customer’s gender and age depending on mobile phone data (2019)Design and Analysis of Computer Experiments (1989)A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010)Global optimization of Lipschitz functions (2017)Practical Bayesian support vector regression for financial time series prediction and market condition change detection (2017)A Multi-Stage Self-Adaptive Classifier Ensemble Model With Application in Credit Scoring (2019)Once Upon a Crime (2014)A survey of results on mobile phone datasets analysis (2015)Combining disparate data sources for improved poverty prediction and mapping (2017)Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance (2021)Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan (2022)Computational Social Science (2009)Private traits and attributes are predictable from digital records of human behavior (2013)Measuring objective and subjective well-being: dimensions and data sources (2020)The Strength of Weak Ties (1973)The CES-D Scale (1977)Time Series Analysis: Forecasting and Control (1977)Unfolding the innovation system for the development of countries: coevolution of Science, Technology and Production (2019)Estimation of Regional Economic Development Indicator from Transportation Network Analytics (2020)Spillovers across industries and regions in China’s regional economic diversification (2021)Mobile Phone Use as Part of Young People's Consumption Styles (2003)Social ties and their relevance to churn in mobile telecom networks (2008)MobiScore: Towards Universal Credit Scoring from Mobile Phone Data (2015)Power-law distributions in empirical data (2018)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)A Tutorial on Bayesian Optimization (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)
this work references cited by second hop

References

  • 2012Practical Bayesian Optimization of Machine Learning Algorithms6,690 cited
  • 2011Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti634 cited
  • 2013Predicting Personality Using Novel Mobile Phone-Based Metrics308 cited
  • 2014BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits248 cited
  • 2014Inferring user demographics and social strategies in mobile social networks203 cited
  • 2010A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records44 cited
  • 2014A study of age and gender seen through mobile phone usage patterns in Mexico43 cited
  • 2017Improving official statistics in emerging markets using machine learning and mobile phone data41 cited
  • 2016bandicoot: a python toolbox for mobile phone metadata34 cited

Cited by

  • 2019Computational socioeconomics115 cited
  • 2021Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach12 cited
  • 2020Link-centric analysis of variation by demographics in mobile phone communication patterns9 cited
  • 2016Estimating individual employment status using mobile phone network data7 cited

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