Data-Driven the Influence of Education Flow of Music Professionals

This study introduces a hybrid recommendation framework leveraging convolutional neural networks (CNNs) for music recommendation, enhancing accuracy and feature analysis. By integrating music tag features into the CNN architecture, personalized recommendations are facilitated, surpassing traditional methods. Additionally, mobility patterns of educational elites in the Yangtze River Delta are explored, illuminating academic cooperation networks. Experimental results demonstrate the effectiveness of the proposed model in predicting user preferences and outperforming existing methods.