Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jakir, K., Fenil, A., Mithila, S.: Different approaches and methods for targeted advertisements by predicting user’s behavioral data and next location. In: Conference 2018, ICISC, pp. 1345–1350. IEEE (2018)
Chen, T., Carlos, G.: Xgboost: a scalable tree boosting system. In: Conference 2016, ACM, pp. 785–794. IEEE (2016)
Ke, G., Meng, Q., Finley, T.: LightGBM: a highly efficient gradient boosting decision tree. In: Conference 2017, NIPS, pp. 342–353. NIPS (2017)
Bi, B., Shokouhi, M., Kosinki, M.: Inferring the demographics of search users: Social data meets search queries. In: Conference 2013, World Wide Web, pp. 131–140. IEEE (2013)
Aarthi, S., Bharanidharan, S., Saravanan, M.: Predicting customer demographics in a mobile social network. In: Conference 2011, International Conference on Advances in Social Networks Analysis and Mining, pp. 553–554. IEEE (2011)
Chen, J., Wang, C., He, K.: Semantics-aware privacy risk assessment using self-learning weight assignment for mobile apps. IEEE Trans. Dependable Secur. Comput. pp, 1 (2018)
Xu, L., Luan, Y., Cheng, X.: Telecom big data based user offloading self-optimisation in heterogeneous relay cellular systems. Int. J. Distrib. Syst. Technol. 8, 27–46 (2017)
Xu, L., Cheng, X., Chen, Y., Chao, K., Liu, D., Xing, H.: Self-optimised coordinated traffic shifting scheme for LTE cellular systems. In: 1st EAI International Conference on Self-Organizing Networks, pp. 67–75. Springer press, Beijing (2015)
Xu, L., Zhao, X., Luan, Y.: User Perception aware telecom data mining and network management for LTE/LTE-advanced networks. In: 4th International Conference on Signal and Information Processing, Networking and Computers, pp. 237–245. Springer press, Qingdao (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, J., Zhang, T., Guan, J., Xu, L., Cheng, X. (2019). A Gender and Age Prediction Algorithm Using Big Data Analytic Based on Mobile APPs Information. In: Sun, S., Fu, M., Xu, L. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2018. Lecture Notes in Electrical Engineering, vol 550. Springer, Singapore. https://doi.org/10.1007/978-981-13-7123-3_60
Download citation
DOI: https://doi.org/10.1007/978-981-13-7123-3_60
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7122-6
Online ISBN: 978-981-13-7123-3
eBook Packages: EngineeringEngineering (R0)