Title: E-commerce collaborative filtering recommendation method based on social network user relationship

Authors: Miao Jiang; Pei Li

Addresses: School of Information and Electronic Engineering, Shangqiu Institute of Technology, Shangqiu, 476000, China ' School of Information and Electronic Engineering, Shangqiu Institute of Technology, Shangqiu, 476000, China

Abstract: In view of the problems of recommendation methods such as poor recommendation correlation coefficient, high recommendation error, and low strength of social network user relations, research on e-commerce collaborative filtering recommendation method based on social network user relationship is proposed. By analysing data related to user e-commerce platforms, calculate the similarity of social user feature data, and extracting social user feature data. By analysing the factors that affect the trust of social network users, intuitive trust is calculated, and multi-dimensional evaluations are conducted to achieve the strength analysis of social network user relationships. Filter the e-commerce product data according to e-commerce collaborative filtering model and build e-commerce collaborative filtering recommendation model. Finally, we realise e-commerce collaborative filtering recommendation. The test results show that the designed method can improve the correlation coefficient of recommended products, and the recommendation effect is good.

Keywords: social networks; user relationships; online retailers; collaborative filtering; recommended methods; user characteristics; evaluation matrix.

DOI: 10.1504/IJNVO.2023.135958

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.341 - 355

Received: 27 Apr 2023
Accepted: 06 Aug 2023
Published online: 10 Jan 2024 *

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