移动 APP 演化模式分析与预测^*

The popularity prediction of mobile APP (application) provides substantial value to a broad range of applications, ranging from APP development to targeted advertising. However, most previous studies do this work by establishing regression models for popularity and impact factors, or using clustering and classification algorithms. It does not fully investigate the process of popularity evolution and the reasons behind it. This paper discusses and analyzes the potential predictors, especially the impact of early evolutionary patterns on future popularity. To this end, this paper first explores six basic evolutionary patterns and six impact factors that are closely related to APP popularity. After detailed analysis, this paper presents CrowdPop, a popularity prediction model based on the random forest algorithm, to quantify patterns and factors as predictors of CrowdPop. The experiment results with a real- world dataset of 126 APPs indicate that, compared with baseline methods, the CrowdPop performs better in mobile APP popularity prediction.