Paper Info
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’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’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.
Year
Venue
Field
2016
ICYCSEE
Rank correlation,App store,Information retrieval,Regression analysis,Computer science,Sentiment analysis,Word error rate,Computer network,Software,Variables,Software versioning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Shanshan Wang100.34
Wenjun Wu222.09
Xuan Zhou300.68