Title: A personalised recommendation algorithm for e-commerce network information based on two-dimensional correlation

Authors: Enwei Cao

Addresses: School of Management and Economics, Jingdezhen Ceramic Institute, Jingdezhen, Jiangxi, 333000, China

Abstract: In view of the poor accuracy and low efficiency of the traditional e-commerce personalised recommendation algorithm, a two-dimensional correlation-based personalised recommendation algorithm for e-commerce network information was proposed. Using two-dimensional correlation, categorise e-commerce user relevancy analysis to measure the personality interests of users in the electronic commerce network, e-commerce project through the Jaccard similarity coefficient, the similarity calculation between the interest spread model was constructed, differentiate the importance of data push grades, and numerical characteristics of e-commerce behaviour are influenced by the importance level, which is calculated by using the sorting result to realise e-commerce personalised recommendation. The experimental results show that the proposed method has high accuracy, diversity and efficiency.

Keywords: two-dimensional correlation; e-commerce network; personalised recommendation of information; interest dissemination; Jaccard similarity coefficient; data push grades.

DOI: 10.1504/IJAACS.2022.127411

International Journal of Autonomous and Adaptive Communications Systems, 2022 Vol.15 No.4, pp.345 - 360

Received: 23 Dec 2019
Accepted: 18 Jul 2020
Published online: 05 Dec 2022 *

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