Open AccessPredict New App Quality By Using Machine Learning
Author(s) -
Zhenghuan Zhong,
Yuhang Bao,
Shiyao Shen,
Entong Zhou
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012111
Subject(s) - computer science , app store , quality (philosophy) , function (biology) , competition (biology) , plan (archaeology) , smartphone app , stock market , artificial intelligence , world wide web , history , ecology , paleontology , philosophy , archaeology , epistemology , horse , evolutionary biology , biology
Increasingly, individuals and companies are developing applications and selling them online. Competition between different companies has led to tens of thousands of applications being put on the market for users to choose from. There are numerous functions on the market to give app evaluation. For users, they do not need to contemplate how to select an app for a long time, because user comments and ratings can give them a clear instruction for user to select an app they need from abundant apps. As a result, we would like to explore the possibility. Hence, we think that we can make a prediction function for the prospect of the app market of whether we can make a prediction function for the app market, akin to a weather forecast or stock market forecast. Our group not only wants to know what the future of the app market is, but also want to find out why some high-quality apps are not getting the downloads they should. With the development of AR and VR technology, the future application will combine with these technology to bring user a immersive experience. In addition, high quality application without AR or VR are less competitive. We plan to answer these questions by using machine learning techniques.