Predicting Age & Gender of Mobile Users at Scale - A Distributed Machine Learning Approach

Democratization of information access brought about by digital distribution has resulted in two contradictory phenomena: the ability to personalize consumer experience, and greater anonymity of users. These intensify when information is consumed on mobile devices, particularly because techniques to profile users on desktop web do not work on mobile smart-devices. Yet, the already large and still fast-growing field of mobile advertising require activation of audience segments against mobile advertising campaigns. Of particular importance are age and gender segments of mobile users, as these user characteristics are required for targeting a large number of ad campaigns. To date, there are no practical methodologies available in the literature that allow for accurate identification of age and gender of mobile users, at scale.In this paper, we propose a scalable machine learning approach to infer the age and gender of mobile users. We have successfully tested and implemented the gender prediction model for 8 countries, additionally the groundwork has been laid out for implementation of age inference. The output is integrated with our commercial products, furthermore, it is used as a part of custom client deliveries. We inferred gender for more than 500 million devices and there is a notable increase in number of devices with gender label (post prediction) within our current dataset. We have also inferred age for 17 million devices in Australia.