Abstract: With the increasing number of online users, constructing user behavior profiles has received widespread attention from relevant scholars. In order to construct user behavior profiles more accurately, the research first designed an adaptive fuzzy neural network algorithm based on the momentum gradient descent method. It uses momentum gradient descent to optimize and learn the parameters adjusted by error backpropagation algorithm and least squares estimation method and optimizes the structure of the fuzzy neural network through subtraction clustering. Finally, the improved algorithm is applied to the construction of user behavior profiles. The results showed that in error analysis, the error range of the improved algorithm was within [-0.10, 0.10], and the accuracy was relatively high. In indicator calculation, the improved algorithm had a recall rate of 0.07 and 0.09 higher than the other two algorithms, an accuracy rate of 0.03 and 0.07 higher than the other two algorithms, and an F1 score of 0.07 and 0.08 higher than the other two algorithms, indicating good overall performance. In the ROC curve, the average detection rate of the designed user behavior profiling model was 0.065 and 0.155 higher than the other two models, respectively, with higher detection accuracy. These results demonstrated the effectiveness of improved algorithms and design models, providing certain reference value for the development of related fields.

Peisen Song, Bengcheng Yu and Chen Chen, “Fuzzy Neural Network Algorithm Application in User Behavior Portrait Construction” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01411119

@article{Song2023,
title = {Fuzzy Neural Network Algorithm Application in User Behavior Portrait Construction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01411119},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01411119},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Peisen Song and Bengcheng Yu and Chen Chen}
}