Demographic information prediction based on smartphone application usage

Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. By considering the category-unbalance problem, we predict users' demographic information and propose an optimization method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).

I. Introduction

Recent technology trends in industry and academia are seeking practice solutions to provide personalized services and improve user experience. For example, Google provides personalized search results, where different users searching for the same terms may receive different responses [1]. Targeted advertising is another example as it recommends proper users to advertisers upon their behaviors on the Internet [2]. As reported by the recent study [3], the targeted advertising is more effective than the ordinary advertisement.