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

<p>In this paper, we first preprocessed the user’s shopping behavior data, set the prediction goal, constructed the features of the user’s online purchasing behavior prediction model, and classified and selected the constructed features based on the SVM-RFE algorithm. Then, on the basis of the fuzzy neural network algorithm of fuzzy theory, the network purchasing behavior prediction model was constructed by combining the assessment indexes of the prediction model results as well as the 5-fold cross-validation method. Finally, the evaluation results of the prediction model are examined and compared with common prediction algorithms to confirm the performance of the algorithm in this paper. The results show that the average relative error of model training can reach 0.013, and the absolute error with the actual value ranges between [0.01, 0.06]. On the same test set, the F1 value of the prediction model in this paper is between [0.88, 0.91], and the F1 value of the algorithm on each test set has a small difference of only 0.03, and the F1 value of the other prediction models has a maximum difference of 0.09. The prediction model constructed in this paper has a good prediction effect and robustness.</p>
</abstract>

In this paper, we first preprocessed the user’s shopping behavior data, set the prediction goal, constructed the features of the user’s online purchasing behavior prediction model, and classified and selected the constructed features based on the SVM-RFE algorithm. Then, on the basis of the fuzzy neural network algorithm of fuzzy theory, the network purchasing behavior prediction model was constructed by combining the assessment indexes of the prediction model results as well as the 5-fold cross-validation method. Finally, the evaluation results of the prediction model are examined and compared with common prediction algorithms to confirm the performance of the algorithm in this paper. The results show that the average relative error of model training can reach 0.013, and the absolute error with the actual value ranges between [0.01, 0.06]. On the same test set, the F1 value of the prediction model in this paper is between [0.88, 0.91], and the F1 value of the algorithm on each test set has a small difference of only 0.03, and the F1 value of the other prediction models has a maximum difference of 0.09. The prediction model constructed in this paper has a good prediction effect and robustness.

Fuzzy Neural Network Algorithm Applied to the Construction of a Prediction Model for Online Buying Behavior

Applied Mathematics and Nonlinear Sciences

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

In this paper, we first preprocessed the user’s shopping behavior data, set the prediction goal, constructed the features of the user’s online purchasing...

Fuzzy Neural Network Algorithm Applied to the Construction of a Prediction Model for Online Buying Behavior

Data publikacji: 18 lis 2023

Zakres stron: -

Otrzymano: 04 sty 2023

Przyjęty: 29 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01167

Słowa kluczowe
SVM-RFE algorithm, 50% discount cross-validation, Fuzzy neural network, Predictive model, Online purchasing behavior

© 2023 Miao Cheng, published by Sciendo

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

Fuzzy Neural Network Algorithm Applied to the Construction of a Prediction Model for Online Buying Behavior

Data publikacji: 18 lis 2023

Zakres stron: -

Otrzymano: 04 sty 2023

Przyjęty: 29 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01167

Słowa kluczoweSVM-RFE algorithm, 50% discount cross-validation, Fuzzy neural network, Predictive model, Online purchasing behavior

© 2023 Miao Cheng, published by Sciendo

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

Słowa kluczowe
SVM-RFE algorithm, 50% discount cross-validation, Fuzzy neural network, Predictive model, Online purchasing behavior