<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Clarifying the correlation between live banding, perceived value, and consumer repurchase is to enhance the economic benefits of live banding better. In this paper, a K-nearest neighbor classification algorithm is proposed in the context of big data analysis technology, and the principle and distance criterion of the algorithm are explained. Then the KNN algorithm is optimized using the Gaussian kernel density function, and the optimization process of the algorithm is given. Finally, the optimized KNN algorithm is used to mine and analyze the indicators of Taobao live banding data, and the performance evaluation is also done for the algorithm. Regarding live-streaming with goods and sensory value, the ratings of A, B, C and D accounted for 38.41%, 36.73%, 34.54% and 35.4%, respectively. In terms of the association between live banding and consumer repurchase, the average value of the data is 33.46%, the maximum value is 43.65%, and the minimum value is 18.16%. Big data analysis shows a strong correlation between live banding, sensory value and consumer repurchase. Live banding enhances consumer sensory value, sensory value influences consumer repurchase, and consumer repurchase behavior promotes live banding optimization, which continuously improves the quality of the product and marketing atmosphere of live banding.</p>
</abstract>

Clarifying the correlation between live banding, perceived value, and consumer repurchase is to enhance the economic benefits of live banding better. In this paper, a K-nearest neighbor classification algorithm is proposed in the context of big data analysis technology, and the principle and distance criterion of the algorithm are explained. Then the KNN algorithm is optimized using the Gaussian kernel density function, and the optimization process of the algorithm is given. Finally, the optimized KNN algorithm is used to mine and analyze the indicators of Taobao live banding data, and the performance evaluation is also done for the algorithm. Regarding live-streaming with goods and sensory value, the ratings of A, B, C and D accounted for 38.41%, 36.73%, 34.54% and 35.4%, respectively. In terms of the association between live banding and consumer repurchase, the average value of the data is 33.46%, the maximum value is 43.65%, and the minimum value is 18.16%. Big data analysis shows a strong correlation between live banding, sensory value and consumer repurchase. Live banding enhances consumer sensory value, sensory value influences consumer repurchase, and consumer repurchase behavior promotes live banding optimization, which continuously improves the quality of the product and marketing atmosphere of live banding.

Big data analysis based on the correlation between live-streaming with goods, perceived value and consumer repurchase

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Clarifying the correlation between live banding, perceived value, and consumer repurchase is to enhance the economic benefits of live banding better. In this...

Big data analysis based on the correlation between live-streaming with goods, perceived value and consumer repurchase

Published Online: Jun 02, 2023

Page range: -

Received: Apr 26, 2022

Accepted: Oct 10, 2022

DOI: https://doi.org/10.2478/amns.2023.1.00134

Keywords
Big data analysis, KNN algorithm, Gaussian kernel function, Live banding, Sensory value

© 2023 Minmin Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Big data analysis based on the correlation between live-streaming with goods, perceived value and consumer repurchase

Published Online: Jun 02, 2023

Page range: -

Received: Apr 26, 2022

Accepted: Oct 10, 2022

DOI: https://doi.org/10.2478/amns.2023.1.00134

KeywordsBig data analysis, KNN algorithm, Gaussian kernel function, Live banding, Sensory value

© 2023 Minmin Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Big data analysis, KNN algorithm, Gaussian kernel function, Live banding, Sensory value