Modeling and forecasting the popularity evolution of mobile apps

In recent years, with the rapid development of mobile app ecosystem, the number and categories of mobile apps have grown tremendously. However, the global prevalence of mobile apps also leads to fierce competition. As a result, many apps will disappear. To thrive in this competitive app market, it is vital for app developers to understand the popularity evolution of their mobile apps, and inform strategic decision-making for better mobile app development. Therefore, it is significant and necessary to model and forecast the future popularity evolution of mobile apps. The popularity evolution of mobile apps is usually a long-term process, affected by various complex factors. However, existing works lack the capabilities to model such complex factors. To better understand the popularity evolution, in this paper, we aim to forecast the popularity evolution of mobile apps by incorporating complex factors, i.e., exogenous stimulis and endogenous excitations. Specifically, we propose a model based on the Multivariate Hawkes Process (MHP), which is an exogenous stimulis-driven self-exciting point process, to model the exogenous stimulis and endogenous excitations simultaneously. Extensive experimental studies on a real-world dataset from app store demonstrate that MHP outperforms the state-of-the-art methods regarding popularity evolution forecasting.