A Gender and Age Prediction Algorithm Using Big Data Analytic Based on Mobile APPs Information

  • Jie GaoEmail author
  • Tao Zhang
  • Jian Guan
  • Lexi Xu
  • Xinzhou Cheng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)


In the current society, almost everyone can’t do without a mobile phone. As the rapidly expansion of smartphone and app market in recently years, the current 35%–40% penetration of smartphone in the mobile phone market will reach to 60% by the year 2019. The customers use their mobile phones to browse internet, have chat and play popular game almost at anywhere and anytime. As a result, mobile phone carries almost all of a person’s behavior and preferences. In that way, user’s personal information such as gender and age, demographic attribute that is frequently used in precision marketing, can be accurately predicted. In this paper, a gender and age prediction algorithm (GAPA) is proposed to predict user’s gender and age by using established supervised machine learning. The numerical results show that the algorithm proposed in this paper is high-efficiency and is able to control the loss function near 2–3.


Big data Data mining Machine learning Prediction algorithm 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jie Gao
    • 1
    Email author
  • Tao Zhang
    • 1
  • Jian Guan
    • 1
  • Lexi Xu
    • 1
  • Xinzhou Cheng
    • 1
  1. 1.Research and Development Centre of Big DataUnicom Network Technology Research Institute ChinaBeijingPeople’s Republic of China

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