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A precision marketing method for digital product big data based on user generated content

Jing Liu et al.

International Journal of Product Development2025https://doi.org/10.1504/ijpd.2025.144847article
AJG 1
Weight
0.37

What the paper says

In order to improve the marketing accuracy and user satisfaction of digital product big data, a precision marketing method based on user generated content for digital product big data is proposed. Firstly, vectorise the user generated evaluation text, digital product category text and image information of digital product descriptions. Secondly, convolutional fusion is performed on the text comprehensive features and image features of digital products. Finally, construct a digital product user interest model based on the level of user interest. Tag weights are used to construct a precise marketing function for digital product big data. The experimental results show that compared with existing marketing methods, this paper's method can improve the marketing accuracy of digital product big data, while also enhancing user satisfaction.

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https://doi.org/https://doi.org/10.1504/ijpd.2025.144847

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@article{jing2025,
  title        = {{A precision marketing method for digital product big data based on user generated content}},
  author       = {Jing Liu et al.},
  journal      = {International Journal of Product Development},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijpd.2025.144847},
}

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A precision marketing method for digital product big data based on user generated content

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.