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VR user experience prediction based on swarm intelligence optimization algorithm to optimize transformer model
- 1 Graduate School of Techno Design, Kookmin University, Seoul, 02707, Korea.
- 2 Graduate School of Techno Design, Kookmin University, Seoul, 02707, Korea.
* Author to whom correspondence should be addressed.
Abstract
This article optimizes the transformer model based on swarm intelligence optimization algorithm to enhance the predictive ability of users' virtual reality (VR) experience. By analyzing the accuracy and loss value changes of the training set, we found that the model showed significant improvement during the training process. Specifically, the accuracy of the training set gradually increased from 62.74% to 86.94% and stabilized; At the same time, the loss value also decreased from 0.71 to 0.37, showing a good convergence trend. This indicates that the model effectively captures important features in the data during the learning process, thereby improving its predictive performance. Further analyzing the confusion matrix of the training set, we can see that 552 VR immersive predictions are correct, while 92 have errors. Among them, 55 instances that should have been predicted as Level 1 immersion were mistakenly classified as Level 2 immersion, while 37 instances that should have been Level 2 immersion were incorrectly predicted as Level 1. This reflects the confusion of the model in certain categories, despite achieving an overall accuracy of 85.69%. On the test set, 230 predicted results were correct, while 45 were not. Among them, 29 instances that should have been level 1 immersion were misclassified as level 2, and 16 instances that should have been level 2 immersion were misclassified as level 1, resulting in an accuracy rate of 83.63% on the test set. In addition, by outputting the ROC curve of the test set with an AUC value of 0.829, it further proves that the model has good classification performance. In summary, this study significantly improved the accuracy of transformer models in predicting VR experiences through swarm intelligence optimization algorithms. This achievement not only validates the effectiveness of swarm intelligence technology in complex data processing, but also provides new ideas and methods for future related fields. With the development of virtual reality technology, accurate prediction of user experience will help improve product design and service quality, thereby promoting the development of the entire industry.
Keywords
Swarm intelligence optimization algorithm, transformer, VR user experience
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Cite this article
Jiang,Q.;Yoon,J.E. (2025). VR user experience prediction based on swarm intelligence optimization algorithm to optimize transformer model. Theoretical and Natural Science,95,1-7.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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