###### Abstract  
In the original publication [1], there was a mistake in Tables 2 and 4 and some texts related to the results of Tables 2 and 4. The error was caused by the incorrect selection of the dataset for testing, so we re-experimented by selecting a test dataset and adding a random seed value for reconstruction. The corrected Table 2 appears below. 28.0822% 38.3562% 1311.7810 s 17 December 2024  
Accepted: 14 January 2025  
Published: 21 February 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. Correction: [PERSON] et al. Automatic Movie Tag Generation System for Improving the Recommendation System. _Appl. Sci._**2022**, _12_, 10777. _Appl. Sci._**2025**, _15_, 2288. [[https://doi.org/10.3390/app155298](https://doi.org/10.3390/app155298)]([https://doi.org/10.3390/app155298](https://doi.org/10.3390/app155298))  
Copyright: 02 2025 by the authors. Uncomsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). In the original publication [1], there was a mistake in Tables 2 and 4 and some texts related to the results of Tables 2 and 4. The error was caused by the incorrect selection of the dataset for testing, so we re-experimented by selecting a test dataset and adding a random seed value for reconstruction. The corrected Table 2 appears below. A correction has been made to Section 4.2, Paragraph 2, \"933\" should be replaced by \"1013\". A correction has been made to Section 5.1, Paragraph 1, \"935\" should be replaced by \"972\" and Paragraph 1, the phrase \"30% of the total\" was changed to \"15% as validation data, and 15% of the total\". In Section 5.1, Paragraph 2, result values were changed. The correct paragraph appears below:  
Table 2 shows the accuracy and learning time of 100 epochs using ResNet34, VGG-19, and MobileNet-v2 models. MobileNet-v2 had 22.6027% accuracy at the first session, and the maximum accuracy is 32.8767%. VGG-19 had 21.9178% accuracy at the first session, and the maximum accuracy is 30.1370%, which is about 2.7% lower than MobileNet-V2. In the case of ResNet34, the first session of accuracy is 28.0822% and the maximum accuracy is 38.3562%, which was about 8.2% higher than VGG-19. As a basis for completing 100 epochs, MobileNet-v2 showed a learning speed of 990.3590 s, VGG-19 showed 4423.7360 s, and ResNet34 showed a learning speed of 1311.7810 s. ResNet34 showed the highest performance in accuracy and MobileNet-v2 showed the highest performance in speed in this study. The corrected Table 4 appears below. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Max Accuracy** & **Learning Time** \\ \hline ResNet34 & 28.0822\% & 38.3562\% & 1311.7810 s \\ \hline VGG-19 & 21.9178\% & 30.1370\% & 4423.7360 s \\ \hline MobileNet & 22.6027\% & 32.8767\% & 990.3590 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy and learning time when running 100 epochs. A correction has been made to Section 5.4, Paragraph 1. The correct paragraph appears below: