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

Yinyue Wan & Pin Lv

International Journal of Web Based Communities2025https://doi.org/10.1504/ijwbc.2025.145139article
ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijwbc.2025.145139

Or copy a formatted citation

@article{yinyue2025,
  title        = {{Personalised recommendation method for live streaming e-commerce products based on multimedia social networks}},
  author       = {Yinyue Wan & Pin Lv},
  journal      = {International Journal of Web Based Communities},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijwbc.2025.145139},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.