@inproceedings{fdd8cccd903846b499df44ce397f5f85,
title = "App store analysis: Using regression model for app downloads prediction",
abstract = "App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don{\textquoteright}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 sentiment analysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman{\textquoteright}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.",
keywords = "App downloads prediction, App store, Regression analysis, Regression model, Spearman{\textquoteright}s rank correlation analysis",
author = "Shanshan Wang and Wenjun Wu and Xuan Zhou",
note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 2nd International Conference on Young Computer Scientists, Engineers and Educators, ICYCSEE 2016 ; Conference date: 20-08-2016 Through 22-08-2016",
year = "2016",
doi = "10.1007/978-981-10-2053-7\_19",
language = "英语",
isbn = "9789811020520",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "206--220",
editor = "Wanxiang Che and Hongzhi Wang and Shaoliang Peng and Weipeng Jing and Guanglu Sun and Xianhua Song and Zeguang Lu and Qilong Han and Junyu Lin and Hongtao Song",
booktitle = "Social Computing - 2nd International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, Proceedings",
address = "德国",
}