Usage profiling from mobile applications: A case study of online activity for Australian primary schools

摘要

Last decade has witnessed a drastically increasing development of smart devices, while related mobile applications have emerged significantly in people’s daily life. As such, understanding the pattern of mobile application usage and related online behavior is of great importance for a variety of purposes, such as application engineering, resource optimization, and marketing. Existing research of online usage discovery includes surveys from end-users, application provider-related analysis, and usage log mining. These works, however, suffer from some limitations, such as lacking of user socio-economics background, insufficient coverage and sample bias, etc.A novel and comprehensive application-usage profiling algorithm, termed as TAG, is proposed in this study to investigate online behavior. The proposed algorithm consists of three major steps: (i) T-step: representing usage data as a Term Frequency-Inverse Document Frequency based matrix; (ii) A-step: applying Alternating Least Squares factorization technique to reduce data sparseness and dimension; and last (iii) G-step: utilizing a smoothed Gaussian Mixture Model for clustering purpose.The performance of the proposed TAG algorithm is evaluated, taking a national dataset generated from 31,280 devices and 30,155 applications over 30 months as an example. Experimental results demonstrate that the proposed algorithm outperforms existing methods via forming accurate usage groups from school-level online behavior. As such, the superior clustering outcome demonstrates the flexibility and applicability of the proposed work for understanding online pattern using complex application usage data. Resultant knowledge can in turn be used to inform decision making and improve application development.