Modeling and Forecasting the Popularity Evolution of Mobile Apps

Ouyang, Y; Guo, B; Guo, T; Cao, L; Yu, Z

Modeling and Forecasting the Popularity Evolution of Mobile Apps

Ouyang, Y Guo, B Guo, T Cao, L

Yu, Z

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, 2018, 2, (4), pp. 1-23
Issue Date:: 2018-12-27

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Field	Value	Language
dc.contributor.author	Ouyang, Y
dc.contributor.author	Guo, B
dc.contributor.author	Guo, T
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.contributor.author	Yu, Z
dc.date.accessioned	2022-08-22T20:57:10Z
dc.date.available	2022-08-22T20:57:10Z
dc.date.issued	2018-12-27
dc.identifier.citation	Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, 2018, 2, (4), pp. 1-23
dc.identifier.issn	2474-9567
dc.identifier.issn	2474-9567
dc.identifier.uri	http://hdl.handle.net/10453/160698
dc.description.abstract	<jats:p>In recent years, with the rapid development of mobile app ecosystem, the number and categories of mobile apps have grown tremendously. However, the global prevalence of mobile apps also leads to fierce competition. As a result, many apps will disappear. To thrive in this competitive app market, it is vital for app developers to understand the popularity evolution of their mobile apps, and inform strategic decision-making for better mobile app development. Therefore, it is significant and necessary to model and forecast the future popularity evolution of mobile apps. The popularity evolution of mobile apps is usually a long-term process, affected by various complex factors. However, existing works lack the capabilities to model such complex factors. To better understand the popularity evolution, in this paper, we aim to forecast the popularity evolution of mobile apps by incorporating complex factors, i.e., exogenous stimulis and endogenous excitations. Specifically, we propose a model based on the Multivariate Hawkes Process (MHP), which is an exogenous stimulis-driven self-exciting point process, to model the exogenous stimulis and endogenous excitations simultaneously. Extensive experimental studies on a real-world dataset from app store demonstrate that MHP outperforms the state-of-the-art methods regarding popularity evolution forecasting.</jats:p>
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
dc.relation.isbasedon	10.1145/3287060
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Modeling and Forecasting the Popularity Evolution of Mobile Apps
dc.type	Journal Article
utslib.citation.volume	2
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-22T20:57:07Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	2
utslib.citation.issue	4

Abstract:

In recent years, with the rapid development of mobile app ecosystem, the number and categories of mobile apps have grown tremendously. However, the global prevalence of mobile apps also leads to fierce competition. As a result, many apps will disappear. To thrive in this competitive app market, it is vital for app developers to understand the popularity evolution of their mobile apps, and inform strategic decision-making for better mobile app development. Therefore, it is significant and necessary to model and forecast the future popularity evolution of mobile apps. The popularity evolution of mobile apps is usually a long-term process, affected by various complex factors. However, existing works lack the capabilities to model such complex factors. To better understand the popularity evolution, in this paper, we aim to forecast the popularity evolution of mobile apps by incorporating complex factors, i.e., exogenous stimulis and endogenous excitations. Specifically, we propose a model based on the Multivariate Hawkes Process (MHP), which is an exogenous stimulis-driven self-exciting point process, to model the exogenous stimulis and endogenous excitations simultaneously. Extensive experimental studies on a real-world dataset from app store demonstrate that MHP outperforms the state-of-the-art methods regarding popularity evolution forecasting.

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http://hdl.handle.net/10453/160698