skip to main content
10.1145/3609437.3609467acmotherconferencesArticle/Chapter ViewAbstractPublication PagesinternetwareConference Proceedingsconference-collections
research-article

A Deep Dive into the Featured iOS Apps

Published: 05 October 2023 Publication History

Abstract

Millions of apps in markets have made it difficult for mobile users to find fancy and high quality apps. Mobile app markets have deployed mechanisms to recommend apps to users. Apple usually features apps in the iOS App Store, and mobile users could see the featured apps as soon as they open the App Store. In general, getting apps featured is an achievement all developers strive towards and it is a common belief that getting app featured means that the app is becoming popular. However, the official app recommendation mechanism has not been characterized yet. To fill the void, we present a large-scale and longitudinal study of featured apps on iOS App Store. Specifically, we collaborate with our industry partner to monitor the iOS App Store and collect the daily featured apps in both the US and China, covering a span of over 1.5 years. Based on this comprehensive dataset, we characterize the featured apps from various dimensions and investigate the impact of app recommendation on app popularity. We have revealed a number of observations that are unknown to the community. Most importantly, we observe that although getting featured indeed has a positive effect for most apps, the duration of this effect is short-lived. In addition, there are times when the recommendations are ineffective, and we propose some potential reasons and tips for this. Our study can offer practical implications on app promotion to stakeholders in the mobile app ecosystem.

References

[1]
2015. Ranking Factors. https://moz.com/blog/app-store-rankings-formula-deconstructed-in-5-mad-science-experiments?_ga=2.18395834.2057695595.1604471809-802527328.1604471809.
[2]
2020. Coronavirus impact sends app downloads, usage and consumer spending to record highs in Q2. https://techcrunch.com/2020/07/09/coronavirus-impact-sends-app-downloads-usage-and-consumer-spending-to-record-highs-in-q2/.
[3]
2021. ARIMA models. https://otexts.com/fpp2/arima.html.
[4]
2022. How long does Apple feature apps in the iOS App store?https://www.quora.com/How-long-does-Apple-feature-apps-in-the-iOS-App-store.
[5]
Mohamed Ali, Mona Erfani Joorabchi, and Ali Mesbah. 2017. Same app, different app stores: A comparative study. In 2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft). IEEE, 79–90.
[6]
Afnan AlSubaihin, Federica Sarro, Sue Black, Licia Capra, and Mark Harman. 2019. App store effects on software engineering practices. IEEE Transactions on Software Engineering (2019).
[7]
Apple. 2021. Discovery on the App Store and Mac App Store. https://developer.apple.com/app-store/discoverability/.
[8]
AppRadar. 2020. Get Your Android App Featured in Google Play: The How To Guide. https://appradar.com/blog/get-your-android-app-featured-in-google-play-the-how-to-guide.
[9]
AppRadar. 2021. How to Get Featured on the App Store. https://appradar.com/blog/how-to-get-featured-in-the-app-store.
[10]
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. the Journal of machine Learning research 3 (2003), 993–1022.
[11]
Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, and Tat-Seng Chua. 2017. Version-sensitive mobile app recommendation. Information sciences 381 (2017), 161–175.
[12]
Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, and Boshen Zhang. 2014. AR-Miner: Mining Informative Reviews for Developers from Mobile App Marketplace. In Proceedings of the 36th International Conference on Software Engineering (Hyderabad, India) (ICSE 2014). Association for Computing Machinery, New York, NY, USA, 767–778. https://doi.org/10.1145/2568225.2568263
[13]
Necmiye Genc-Nayebi and Alain Abran. 2017. A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software 125 (2017), 207–219. https://doi.org/10.1016/j.jss.2016.11.027
[14]
Alessandra Gorla, Ilaria Tavecchia, Florian Gross, and Andreas Zeller. 2014. Checking app behavior against app descriptions. In Proceedings of the 36th international conference on software engineering. 1025–1035.
[15]
Mark Harman, Yue Jia, and Yuanyuan Zhang. 2012. App store mining and analysis: MSR for app stores. In 2012 9th IEEE working conference on mining software repositories (MSR). IEEE, 108–111.
[16]
Apple Inc.2021. Getting Featured on the App Store. https://developer.apple.com/app-store/getting-featured/.
[17]
Alexandros Karatzoglou, Linas Baltrunas, Karen Church, and Matthias Böhmer. 2012. Climbing the app wall: enabling mobile app discovery through context-aware recommendations. In Proceedings of the 21st ACM international conference on Information and knowledge management. 2527–2530.
[18]
Fuqi Lin, Haoyu Wang, Liu Wang, and Xuanzhe Liu. 2021. A longitudinal study of removed apps in ios app store. In Proceedings of the Web Conference 2021. 1435–1446.
[19]
Bin Liu, Deguang Kong, Lei Cen, Neil Zhenqiang Gong, Hongxia Jin, and Hui Xiong. 2015. Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the eighth ACM international conference on web search and data mining. 315–324.
[20]
William Martin, Mark Harman, Yue Jia, Federica Sarro, and Yuanyuan Zhang. 2015. The app sampling problem for app store mining. In 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories. IEEE, 123–133.
[21]
William Martin, Federica Sarro, Yue Jia, Yuanyuan Zhang, and Mark Harman. 2016. A survey of app store analysis for software engineering. IEEE transactions on software engineering 43, 9 (2016), 817–847.
[22]
Anuja Nagpal. 2017. L1 and L2 Regularization Methods. https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c.
[23]
Rahul Pandita, Xusheng Xiao, Wei Yang, William Enck, and Tao Xie. 2013. { WHYPER} : Towards automating risk assessment of mobile applications. In 22nd { USENIX} Security Symposium ({ USENIX} Security 13). 527–542.
[24]
Abbas Razaghpanah, Rishab Nithyanand, Narseo Vallina-Rodriguez, Srikanth Sundaresan, Mark Allman, Christian Kreibich, and Phillipa Gill. 2018. Apps, trackers, privacy, and regulators: A global study of the mobile tracking ecosystem. (2018).
[25]
Joel Reardon, Álvaro Feal, Primal Wijesekera, Amit Elazari Bar On, Narseo Vallina-Rodriguez, and Serge Egelman. 2019. 50 ways to leak your data: An exploration of apps’ circumvention of the android permissions system. In 28th { USENIX} Security Symposium ({ USENIX} Security 19). 603–620.
[26]
Israel J Mojica Ruiz, Meiyappan Nagappan, Bram Adams, Thorsten Berger, Steffen Dienst, and Ahmed E Hassan. 2014. Impact of ad libraries on ratings of Android mobile apps. IEEE Software 31, 6 (2014), 86–92.
[27]
Rocky Slavin, Xiaoyin Wang, Mitra Bokaei Hosseini, James Hester, Ram Krishnan, Jaspreet Bhatia, Travis D Breaux, and Jianwei Niu. 2016. Toward a framework for detecting privacy policy violations in Android application code. In Proceedings of the 38th International Conference on Software Engineering. 25–36.
[28]
Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, 2020. Tslearn, A Machine Learning Toolkit for Time Series Data.J. Mach. Learn. Res. 21, 118 (2020), 1–6.
[29]
Haoyu Wang, Hao Li, and Yao Guo. 2019. Understanding the evolution of mobile app ecosystems: A longitudinal measurement study of google play. In The World Wide Web Conference. ACM, 1988–1999.
[30]
Haoyu Wang, Zhe Liu, Yao Guo, Xiangqun Chen, Miao Zhang, Guoai Xu, and Jason Hong. 2017. An explorative study of the mobile app ecosystem from app developers’ perspective. In Proceedings of the 26th International Conference on World Wide Web. 163–172.
[31]
Haoyu Wang, Zhe Liu, Jingyue Liang, Narseo Vallina-Rodriguez, Yao Guo, Li Li, Juan Tapiador, Jingcun Cao, and Guoai Xu. 2018. Beyond google play: A large-scale comparative study of chinese Android app markets. In Proceedings of the Internet Measurement Conference 2018. 293–307.
[32]
Liu Wang, Haoyu Wang, Xiapu Luo, Tao Zhang, Shangguang Wang, and Xuanzhe Liu. 2022. Demystifying “removed reviews” in iOS app store. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 1489–1499.
[33]
Wikipedia. 2021. App Store (iOS/iPadOS). https://en.wikipedia.org/wiki/App_Store_(iOS/ iPadOS).
[34]
Wikipedia. 2021. Elbow method (clustering). https://en.wikipedia.org/wiki/Elbow_method_ (clustering).
[35]
Hongzhi Yin, Liang Chen, Weiqing Wang, Xingzhong Du, Quoc Viet Hung Nguyen, and Xiaofang Zhou. 2017. Mobi-sage: A sparse additive generative model for mobile app recommendation. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE, 75–78.
[36]
Le Yu, Xiapu Luo, Xule Liu, and Tao Zhang. 2016. Can we trust the privacy policies of Android apps?. In 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 538–549.

Index Terms

  1. A Deep Dive into the Featured iOS Apps

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    Internetware '23: Proceedings of the 14th Asia-Pacific Symposium on Internetware
    August 2023
    332 pages
    ISBN:9798400708947
    DOI:10.1145/3609437
    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 the author(s) 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].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 05 October 2023

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. app mining
    2. app store
    3. featured apps
    4. iOS

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    Internetware 2023

    Acceptance Rates

    Overall Acceptance Rate 55 of 111 submissions, 50%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 65
      Total Downloads
    • Downloads (Last 12 months)44
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media