App Store Analysis: Using Regression Model for App Downloads Prediction

• 作者: Shanshan Wang Wenjun Wu Xuan Zhou
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: APP STORE Spearman’s RANK CORRELATION ANALYSIS Regression ANALYSIS Regression model APP downloads PREDICTION
• 年，卷（期）: 2016,（1）
• 页码范围: 54-56
• 页数: 3页
• 分类号: C5

摘要

App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app description and app name currently. In this paper we propose an approach that App Store Analysis can be used to predict app downloads. We use data mining to extract app name and description and app rank information etc. from the Wandoujia App Store and AppCha App Store. We use questionnaire and sentimentanalysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman’s rank correlation analysis respectively. Finally, we establish a multiple nonlinear regression model which app downloads defined as dependent variable and three relevant attributes defined as independent variable. On average, 59.28 % of apps in Wandoujia App Store and 66.68 % of apps in AppCha App Store can be predicted accurately within threshold which error rate is 25 %. One can observe the more detailed classification of app store, the more accurate for regression modeling to predict app downloads. Our approach can help app developers to notice and optimize the vital factors which influence app downloads.