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PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications
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AI
DOI：10.1109/ACCESS.2021.3053583.png)
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摘要
En 中文
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.
Keyword:
Mobile applications
Probabilistic logic
Hidden Markov models
Recommender systems
Predictive models
Collaboration
Computer science
Mobile apps recommendation
matrix factorization
probabilistic matrix factorization
version sensitive recommendation
probabilistic evolution based version recommendation model
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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