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

A personalised recommendation algorithm for e-commerce network information based on... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Autonomous and Adaptive Communications Systems Inderscience Publishers

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

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1754-8632
eISSN
1754-8640
DOI
10.1504/ijaacs.2022.127411
Publisher site
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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.

Journal

International Journal of Autonomous and Adaptive Communications Systems – Inderscience Publishers

Published: Jan 1, 2022

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