E-commerce collaborative filtering recommendation method based on social network user relationship
by Miao Jiang; Pei Li
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 29, No. 3/4, 2023

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.

Online publication date: Wed, 10-Jan-2024

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