计算机科学与探索 ›› 2019, Vol. 13 ›› Issue (12): 1981-1994.DOI: 10.3778/j.issn.1673-9418.1812034

• 学术研究 • 上一篇    下一篇

移动APP演化模式分析与预测

张艺璇,郭斌,欧阳逸,王柱,於志文   

  1. 西北工业大学 计算机学院,西安 710072
  • 出版日期:2019-12-01 发布日期:2019-12-10

Evolutionary Pattern Analysis and Prediction of Mobile APP

ZHANG Yixuan, GUO Bin, OUYANG Yi, WANG Zhu, YU Zhiwen   

  1. School of Computer Science, Northwestern Polytechnical University, Xi??an 710072, China
  • Online:2019-12-01 Published:2019-12-10

摘要: 移动APP的流行度预测工作从应用开发到广告投放都具有巨大的应用价值。然而,多数先前的工作都是建立影响因子与流行度之间的回归模型或者采用聚类和分类算法,这样做忽略流行度演化的过程以及背后的原因。讨论并分析潜在的预测因子,特别是早期流行度的演化模式对未来流行度的影响。为此,首先探索6种与APP流行度密切相关的演化模式和6种影响因素。经过详细分析后提出基于随机森林算法的流行度预测模型CrowdPop,并量化演化模式和影响因素作为CrowdPop的预测因子。实验结果显示在126个不同种类的APP中,CrowdPop针对APP流行度的预测精度优于基准方法。

关键词: 移动APP, 演化模式, 影响因素, 流行度预测

Abstract: 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.

Key words: mobile APP, evolution pattern, impact factors, popularity prediction