Modeling the Temporal Nature of Human Behavior for Demographics Prediction

Bjarke Felbo, Pål Sundsøy, Alex Pentland, Sune Lehmann Jørgensen, Yves-Alexandre Montjoye

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent from mobile phone datasets, limiting the operational impact of the datasets. For these reasons, there has been a growing interest in predicting demographic information from mobile phone metadata. Previous work focused on creating increasingly advanced features to be modeled with standard machine learning algorithms. We here instead model the raw mobile phone metadata directly using deep learning, exploiting the temporal nature of the patterns in the data. From high-level assumptions we design a data representation and convolutional network architecture for modeling patterns within a week. We then examine three strategies for aggregating patterns across weeks and show that our method reaches state-of-the-art accuracy on both age and gender prediction using only the temporal modality in mobile metadata. We finally validate our method on low activity users and evaluate the modeling assumptions.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
EditorsYasemin Altun, Kamalika Das, Taneli Mielikäinen, Donato Malerba, Jerzy Stefanowski, Jesse Read, Marinka Žitnik, Michelangelo Ceci, Sašo Džeroski
Number of pages13
Volume10536
PublisherSpringer
Publication date2017
Pages140-152
ISBN (Print)978-3-319-71272-7
ISBN (Electronic)978-3-319-71273-4
DOIs
Publication statusPublished - 2017
EventThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2017 - Skopje, Macedonia, The Former Yugoslav Republic of
Duration: 18 Sep 201722 Sep 2017

Conference

ConferenceThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2017
CountryMacedonia, The Former Yugoslav Republic of
CitySkopje
Period18/09/201722/09/2017
SeriesLecture Notes in Computer Science
ISSN0302-9743

Keywords

  • Call Detail Records
  • Mobile phone metadata
  • Temporal patterns
  • User modeling
  • Demographics prediction

Cite this

Felbo, B., Sundsøy, P., Pentland, A., Jørgensen, S. L., & Montjoye, Y-A. (2017). Modeling the Temporal Nature of Human Behavior for Demographics Prediction. In Y. Altun, K. Das, T. Mielikäinen, D. Malerba, J. Stefanowski, J. Read, M. Žitnik, M. Ceci, & S. Džeroski (Eds.), Machine Learning and Knowledge Discovery in Databases (Vol. 10536, pp. 140-152). Springer. Lecture Notes in Computer Science https://doi.org/10.1007/978-3-319-71273-4_12