Title: Design of multivariable big data mobile analysis platform based on collaborative filtering recommendation algorithm

Authors: Yan Liu; Jun Tang; Jue Lei

Addresses: Department of Software, Hunan Vocational College of Science and Technology, Changsha, Hunan, 410118, China ' Department of Software, Hunan Vocational College of Science and Technology, Changsha, Hunan, 410118, China ' Department of Software, Hunan Vocational College of Science and Technology, Changsha, Hunan, 410118, China

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%.

Keywords: collaborative filtering recommendation; multivariate; big data; mobile analysis platform; data dimensionality reduction.

DOI: 10.1504/IJAACS.2020.109811

International Journal of Autonomous and Adaptive Communications Systems, 2020 Vol.13 No.2, pp.116 - 134

Received: 23 Aug 2019
Accepted: 20 Nov 2019

Published online: 24 Sep 2020 *

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