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PRADO: Predicting App Adoption by Learning the Correlation between Developer-Controllable Properties and User Behaviors

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Qiaozhu MeiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 79, Pages 1 - 30

https://doi.org/10.1145/3130944

Published: 11 September 2017 Publication History

Abstract

To survive and stand out from the fierce market competition nowadays, it is critical for app developers to know (desirably ahead of time) whether, how well, and why their apps would be adopted by users. Ideally, the adoption of an app could be predicted by factors that can be controlled by app developers in the development process, and factors that app developers are able to take actions on and improve according to the predictions. To this end, this paper proposes PRADO, an approach to measuring various aspects of user adoption, including app download and installation, uninstallation, and user ratings. PRADO employs advanced machine learning algorithms to predict user adoption based on how these metrics correlate to a comprehensive taxonomy of 108 developer-controllable features of the app. To evaluate PRADO, we use 9,824 free apps along with their behavioral data from 12.57 million Android users, demonstrating that user adoption of a new app can be accurately predicted. We also derive insights on which factors are statistically significant to user adoption, and suggest what kinds of actions can be possibly performed by developers in practice.

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Su S(2024)Research on Visual Design of Smart Phone APP Interface Based on Human-Computer Interaction BehaviorInternational Journal of e-Collaboration10.4018/IJeC.34973720:1(1-14)Online publication date: 30-Jul-2024
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Index Terms

PRADO: Predicting App Adoption by Learning the Correlation between Developer-Controllable Properties and User Behaviors

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 May 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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the Natural Science Foundation of China
the National Science Foundation
the High-Tech Research and Development Program of China
the MCubed grant at the University of Michigan
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the Microsoft-PKU Joint Research Program

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https://doi.org/10.4018/IJeC.349737
Wimalasooriya CLicorish Sda Costa DMacDonell S(2024)Just-in-Time crash prediction for mobile appsEmpirical Software Engineering10.1007/s10664-024-10455-729:3Online publication date: 8-May-2024
https://dl.acm.org/doi/10.1007/s10664-024-10455-7
Lin FLu XAi WLi HMa YYang YDeng HWang QMei QLiu X(2023)Adoption of Recurrent Innovations: A Large-Scale Case Study on Mobile App UpdatesACM Transactions on the Web10.1145/362618918:1(1-26)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3626189
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Liang YLiu LHuangfu LWang ZGuo B(2023)DeepApp: characterizing dynamic user interests for mobile application recommendationWorld Wide Web10.1007/s11280-023-01161-326:5(2623-2645)Online publication date: 2-May-2023
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