PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications
subject: matrix factorization; Mobile apps recommendation; probabilistic evolution based version recommendation model; probabilistic matrix factorization; version sensitive recommendation
description: 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. 2013 IEEE.
creator: Maheswari M.; Geetha S.; Selva Kumar S.; Karuppiah M.; Samanta D.; Park Y.
source: IEEE Access, Vol-9, pp. 20819-20827.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2021-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 21693536
format: Online
language: English
type: Article
coverage: Maheswari M., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119, India; Geetha S., School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India; Selva Kumar S., Department of Computer Science and Engineering, GKM College of Engineering and Technology, Chennai, 600063, India; Karuppiah M., Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, 201204, India; Samanta D., Department of Computer Science, CHRIST University, Bengaluru, 560029, India; Park Y., School of Computer Engineering, Keimyung University, Daegu, 42601, South Korea