Using cloud computing platform of 6G IoT in e-commerce personalized recommendation

Junhai Wang and Yiman Zhang ()
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Junhai Wang: Zhejiang Technical Institute of Economics
Yiman Zhang: Wenzhou University

International Journal of System Assurance Engineering and Management, 2021, vol. 12, issue 4, No 4, 654-666

Abstract: Abstract In order to improve the exposure of commodities in e-commerce, accurately recommend personalized commodities and stimulate users' consumption of commodities, on the basis of a lot of literature, a personalized recommendation framework for e-commerce is built based on the open-source Hadoop cloud computing platform. According to the similarity of the algorithm, through the comparison of the project collaborative filtering algorithm based on cloud computing, user collaborative algorithm and the improved algorithm based on matrix filling and time context, the optimal algorithm is obtained and evaluated comprehensively in two aspects of algorithm performance and personalized recommendation performance. The results show that compared with UBCF (User Based Collaborative Filtering) and IBCF (Item Based Collaborative Filtering), the IA (Improved algorithm) based on matrix filling and time context is more accurate, its MAE (Mean absolute error) is smaller, and the scalability is enhanced. With the increase of nodes, the operation efficiency of the algorithm is obviously improved. The data recommendation effect of IA algorithm is better than that of IBCF and UBCF, and the commodities recommended to consumers are more in line with their preferences, which can improve the novelty and coverage rate of the algorithm, improve the exposure of the commodities in all aspects, and greatly promote the consumer behavior of users. The data mining system based on Hadoop cloud computing is introduced into e-commerce, which can provide theoretical basis for related research, and it is also of great significance to social development and academic research.

Keywords: Hadoop cloud computing; Deep learning; E-commerce; Personalized recommendation; Matrix filling; Time context (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13198-021-01059-1

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