Modeling and forecasting the popularity evolution of mobile apps

Yi Ouyang, Bin Guo*, Tong Guo, Longbing Cao, Zhiwen Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number182
Pages (from-to)1-23
Number of pages23
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Issue number4
Publication statusPublished - Dec 2018
Externally publishedYes


  • Mobile Apps
  • Popularity Evolution
  • Hawkes Process
  • Exogenous Stimulis
  • Endogenous Excitations


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