Title: Personalised recommendation method for live streaming e-commerce products based on multimedia social networks

Authors: Yinyue Wan; Pin Lv

Addresses: Science and Technology Division, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China ' Center for Humanistic Quality Education, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China

Abstract: There are problems in personalised recommendation of live streaming e-commerce products, such as low accuracy in user interest mining and weak user relationship strength. Therefore, a personalised recommendation method for live streaming e-commerce products based on multimedia social networks is proposed. First, the user scoring matrix is divided into two interaction matrices by the matrix decomposition method, and the fixed parameter limit matrix dimension is set, and user interest mining is realised by using Euclidean distance calculation. Then, the variance expansion factor is introduced to test the multi-collinearity of the feature, and the contour coefficient is calculated to complete the feature extraction. Finally, user interest and feature data are introduced into multimedia social networks to obtain product feature attention, perform personalised matching, and achieve personalised recommendation. The results show that the method proposed in this paper has good user interest mining performance and strong user relationships.

Keywords: multimedia social network; live streaming e-commerce products; personalised recommendation; interest level; variance inflation factor; attention level.

DOI: 10.1504/IJWBC.2025.145139

International Journal of Web Based Communities, 2025 Vol.21 No.1/2, pp.2 - 19

Received: 12 Jun 2023
Accepted: 07 Nov 2023
Published online: 21 Mar 2025 *

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