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Study on detection of impulsive purchase behaviour of e-commerce platform consumers based on social network media

Bo An

International Journal of Web Based Communities2025https://doi.org/10.1504/ijwbc.2025.147389article
ABDC C
Weight
0.50

What the paper says

Studying consumers' impulsive purchasing behaviour helps to understand their purchasing behaviour and increase sales revenue. Therefore, this article proposes a method for detecting consumer impulse buying behaviour on e-commerce platforms based on social network media. Firstly, collect data on consumer purchasing behaviour. Secondly, preprocess the characteristics of impulse buying behaviour based on the RFM function. Then, considering the polarity and degree of emotional words, calculate impulsive emotion scores based on an emotion dictionary. Finally, use the LSH algorithm to find the nearest neighbour point that matches each user's emotional needs, and use the input of LOF to find the extreme point, obtaining the detection results of impulse buying behaviour. The results show that the detection recall rate of this method can reach 99.0%, the detection error is only 0.02, and the detection time is only 8.9 seconds. The detection effect of this method is good.

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https://doi.org/https://doi.org/10.1504/ijwbc.2025.147389

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@article{bo2025,
  title        = {{Study on detection of impulsive purchase behaviour of e-commerce platform consumers based on social network media}},
  author       = {Bo An},
  journal      = {International Journal of Web Based Communities},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijwbc.2025.147389},
}

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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.