A personalised recommendation algorithm for e-commerce network information based on two-dimensional correlation. (27th November 2022)

• Record Type: Journal Article
• Authors: Cao, Enwei
• Is Part Of: International journal of autonomous and adaptive communications systems. Volume 15:Number 4(2022)
• Page Start: 345
• Page End: 360
• Publication Date: 2022-11-27
• Subjects: two-dimensional correlation -- e-commerce network -- personalised recommendation of information -- interest dissemination -- Jaccard similarity coefficient -- data push grades; Adaptive computing systems -- Periodicals; Wireless communication systems -- Periodicals; Computer networks -- Periodicals; 004.6
• Languages: English
• ISSNs: 1754-8632

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.