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

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.