Neural network models for predicting trends in VTuber Industry on Bilibili

Jiaqi Chen

doi:10.1117/12.2659194

23 November 2022 Neural network models for predicting trends in VTuber Industry on Bilibili

Jiaqi Chen

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 1245423 (2022) https://doi.org/10.1117/12.2659194
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

Due to the paucity of data for this VTuber industry and the volatility of the data itself, in recent years the processing of datasets in the VTuber industry has remained at the statistical stage, and providing accurate forecasts of industry trends has been quite challenging. In this paper, a new way of thinking about dealing with the VTuber industry on Bilibili datasets is conceived, namely using Neural Networks (NN) and machine learning algorithms to implement predictions. Bilibili is a well-known video-sharing website and also the largest live streaming platform for VTubers in China. More specifically, the prediction of datasets in the VTuber industry using the Long Short-Term Memory (LSTM) model is done based on the TensorFlow machine learning framework. In short, the dataset is processed to match the dimensions of the TensorFlow library input by dividing and preprocessing the training and test datasets, and then the prediction is implemented by the LSTM model. Finally, the error metrics are calculated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). The experimental results show that the LSTM model achieves a very high accuracy match in predicting the VTuber industry dataset, achieves a more competitive performance in data processing, and can provide more effective industry predictions for those engaged in the VTuber industry.

Citation Download Citation

Jiaqi Chen "Neural network models for predicting trends in VTuber Industry on Bilibili", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 1245423 (23 November 2022); https://doi.org/10.1117/12.2659194

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