Design of multivariable big data mobile analysis platform based on collaborative filtering recommendation algorithm Online publication date: Thu, 24-Sep-2020
by Yan Liu; Jun Tang; Jue Lei
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 13, No. 2, 2020
Abstract: In order to overcome the problems of poor accuracy and high data redundancy in the current big data analysis platform, this paper proposes and designs a multivariable big data mobile analysis platform based on collaborative filtering recommendation algorithm. The platform is divided into data acquisition layer, storage layer, processing and analysis layer and scheduling layer, introduces two ways of dimensionality reduction and recommendation to realise multivariable big data mining and analysis. User behaviour analysis and data item behaviour analysis of the dimension-reduced data are carried out, and multi-level coordination is used to complete the construction of multivariable big data mobile analysis platform. The experimental results show that the accuracy of the platform's big data analysis is always above 97%, and the accuracy of data mining analysis is stronger. The acceleration ratio is always above 2, the response speed is faster, the user satisfaction is about 96%.
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