Deep learning-based quantity of talent demand prediction

Deep learning-based quantity of talent demand prediction

연구 분야: Artificial Intelligence

학회: IoTML '25: Proceedings of the 2025 5th International Conference on Internet of Things and Machine Learning

Talent demand prediction is very important for national development strategy adjustment and social and economic development. Notably, this type of prediction is useful for industries to adjust their recruitment strategies and professionals to plan their careers. Therefore, automatically predicting the talent demand in society is necessary and may help job seekers to find a job. Many methods have been proposed to predict the talent demand. However, the accuracy of current methods needs to be improved. Therefore, in this paper, we propose a novel approach that uses a deep learning method to predict the talent demand in industry. First, we collect and build a talent demand data, build a corresponding dataset and preprocess the dataset. Second, we perform data modeling to prepare the input data for the deep learning model. Finally, we pass the modeled data to a specially designed deep neural network-based model to predict the quantity of talent. We also evaluate the proposed approach on the basis of the constructed dataset. The evaluation results suggest that the proposed method is accurate and outperforms state-of-the-art approaches. On average, it significantly reduces the mean absolute error by more than 26%, reduces the mean square error by more than 11%, and increases the squared correlation coefficient by more than 13%.