GinApp: An Inductive Graph Learning based Framework for Mobile Application Usage Prediction

Mobile application usage prediction aims to infer the possible applications (Apps) that a user will launch next. It is critical for many applications, e.g., system optimization and smartphone resource management. Recently, graph based App prediction approaches have been proved effective, but still suffer from several issues. First, these studies cannot naturally generalize to unseen Apps. Second, they do not model asymmetric transitions between Apps. Third, they are hard to differentiate the contributions of different App usage context on the prediction result. In this paper, we propose GinApp, an inductive graph representation learning based framework, to resolve these issues. Specifically, we first construct an attribute-aware directed graph based on App usage records, where the App-App transitions and times are modeled by directed weighed edges. Then, we develop an inductive graph learning based method to generate effective node representations for the unseen Apps via sampling and aggregating information from neighboring nodes. Finally, our App usage prediction task is formulated as a link prediction problem on graph to generate the Apps with the largest probabilities as prediction results. Extensive experiments on two large-scale App usage datasets reveal that GinApp provides the state-of-the-art performance for App usage prediction.