Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine

Rendao Ye (), Mengyao Yang and Peng Sun
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Rendao Ye: School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China
Mengyao Yang: School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China
Peng Sun: School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China

Sustainability, 2023, vol. 15, issue 20, 1-17

Abstract: The traditional e-commerce business chain is being reconstructed around the content of short videos and live streams, and the interest e-commerce is thriving as a new trend in the e-commerce industry. Diversified content promotes the rapid development of interest e-commerce. For consumers, their preferences for different content reflect their consumption level to a certain extent. The purpose of this study is to accurately predict the purchasing power level with the consumer content preference, and provide new ideas for interest e-commerce business. In this paper, the new swarm intelligence algorithm is used to find the optimal misclassification cost, and three cost-sensitive models are established. On this basis, the content preference of interest e-commerce consumers is used to predict the level of purchasing power. The results show that the content preference of interest e-commerce consumers, such as “fashion”, “photography” and “interpretation”, have a significant effect on the prediction of purchasing power at the 95% confidence level. The accuracies of the optimized cost-sensitive support vector machine in predicting consumer purchasing power are all above 0.9, and the highest is 0.9792. This study effectively alleviates the problem that the classification results tend to be biased towards negative samples, especially when the imbalanced rate of the sample is high. It not only provides researchers with an efficient parameter optimization method, but also reflects the relationship between consumer content preference and purchasing power, providing data support for interest e-commerce operations.

Keywords: content preference; purchasing power; swarm intelligence algorithm; cost-sensitive support vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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