Mining mobile application usage pattern for demand prediction by considering spatial and temporal relations

Recently, researches on smart phones have received attentions because the wide potential applications. One of interesting and useful topic is mining and predicting the users’ mobile application (App) usage behaviors. With more and more Apps installed in users’ smart phone, the users may spend much time to find the Apps they want to use by swiping the screen. App prediction systems benefit for reducing search time and launching time since the Apps which may be launched can preload in the memory before they are actually used. Although some previous studies had been proposed on the problem of App usage analysis, they recommend Apps for users only based on the frequencies of App usages. We consider that the relationship between App usage demands and users’ recent spatial and temporal behaviors may be strong. In this paper, we propose Spatial and Temporal App Recommender (STAR), a novel framework to predict and recommend the Apps for mobile users under a smart phone environment. The STAR framework consists of four major modules. We first find the meaningful and semantic location movements from the geographic GPS trajectory data by the Spatial Relation Mining Module and generate the suitable temporal segments by the Temporal Relation Mining Module. Then, we design Spatial and Temporal App Usage Pattern Mine (STAUP-Mine) algorithm to efficiently discover mobile users’ Spatial and Temporal App Usage Patterns (STAUPs). Furthermore, an App Usage Demand Prediction Module is presented to predict the following App usage demands according to the discovered STAUPs and spatial/temporal relations. To our knowledge, this is the first study to simultaneously consider the spatial movements, temporal properties and App usage behavior for mining App usage pattern and demand prediction. Through rigorous experimental analysis from two real mobile App datasets, STAR framework delivers an excellent prediction performance.