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App mining: finding the real value of mobile applications

Authors:

Peng Yu,

Ching-man Au YeungAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 417 - 418

https://doi.org/10.1145/2567948.2577297

Published: 07 April 2014 Publication History

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Abstract

In this poster, we present a new model for estimating the actual value of mobile apps to the users. The model assumes that users are implicitly evaluating the value of the apps in their smartphones when they choose to uninstall some apps. Our proposed method thus makes use of the install and uninstall log in a mobile app store to estimate the value of the apps. Experiments using data from a popular mobile app store show that our model is better in predicting the future download trend of the apps as well as the future uninstallation rate of the apps. We believe such model will be very useful in generating more credible and appropriate mobile app recommendations to users, or in generating features for machine learning systems in more complex prediction tasks.

References

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W. Woerndl, C.Schueller, and R. Wojtech. A hybrid recommender system for context-aware recommendations of mobile applications. In Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop, ICDEW '07, pages 871--878, Washington, DC, USA, 2007. IEEE Computer Society.

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P. Yin, P. Luo, W.-C. Lee, and M. Wang. App recommendation: a contest between satisfaction and temptation. In Proceedings of the sixth ACM international conference on Web search and data mining, WSDM '13, pages 395--404, New York, NY, USA, 2013. ACM.

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Cited By

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(2017)Mining smartphone data for app usage prediction and recommendationsPervasive and Mobile Computing10.1016/j.pmcj.2017.01.00737:C(1-22)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.pmcj.2017.01.007
Elahi HWang GLi X(2017)Smartphone Bloatware: An Overlooked Privacy ProblemSecurity, Privacy, and Anonymity in Computation, Communication, and Storage10.1007/978-3-319-72389-1_15(169-185)Online publication date: 7-Dec-2017
https://doi.org/10.1007/978-3-319-72389-1_15
Xia XShihab EKamei YLo DWang XGenero MJedlitschka AJørgensen MScanniello GSampath SCaivano DPort D(2016)Predicting Crashing Releases of Mobile ApplicationsProceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/2961111.2962606(1-10)Online publication date: 8-Sep-2016
https://dl.acm.org/doi/10.1145/2961111.2962606
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Index Terms

App mining: finding the real value of mobile applications
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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WWW '14

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IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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(2017)Mining smartphone data for app usage prediction and recommendationsPervasive and Mobile Computing10.1016/j.pmcj.2017.01.00737:C(1-22)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.pmcj.2017.01.007
Elahi HWang GLi X(2017)Smartphone Bloatware: An Overlooked Privacy ProblemSecurity, Privacy, and Anonymity in Computation, Communication, and Storage10.1007/978-3-319-72389-1_15(169-185)Online publication date: 7-Dec-2017
https://doi.org/10.1007/978-3-319-72389-1_15
Xia XShihab EKamei YLo DWang XGenero MJedlitschka AJørgensen MScanniello GSampath SCaivano DPort D(2016)Predicting Crashing Releases of Mobile ApplicationsProceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/2961111.2962606(1-10)Online publication date: 8-Sep-2016
https://dl.acm.org/doi/10.1145/2961111.2962606
Rana JBjelland JCouronne TSundsøy PWagner DRice A(2014)A Handset-centric View of Smartphone Application UseProcedia Computer Science10.1016/j.procs.2014.07.03934(368-375)Online publication date: 2014
https://doi.org/10.1016/j.procs.2014.07.039

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