PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications

Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.20819-20827
Main Authors: Maheswari, M., Geetha, S., Kumar, S. Selva, Karuppiah, Marimuthu, Samanta, Debabrata, Park, Yohan
Format: Article
Language:English
Subjects:
Algorithms
Applications programs
Cold starts
Collaboration
Computer science
Evolution
Hidden Markov models
matrix factorization
Mobile applications
Mobile apps recommendation
Mobile computing
Predictive models
probabilistic evolution based version recommendation model
Probabilistic logic
probabilistic matrix factorization
Recommender systems
version sensitive recommendation
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Summary:Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named "Probabilistic Evolution based Version Recommendation Model (PEVRM)" integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3053583