The analysis of artificial intelligence knowledge graphs for online music learning platform under deep learning

Area of Science:

• Artificial Intelligence
• Music Information Retrieval
• Machine Learning

Background:

• Personalized recommendations are crucial for user engagement in digital learning platforms.
• Existing music recommendation systems often struggle with data sparsity and lack deep semantic understanding.
• Integrating diverse data types (audio, video, user behavior) is key to improving recommendation accuracy.

Purpose of the Study:

• To develop a deep learning-based personalized music learning platform model.
• To enhance recommendation accuracy and personalization by integrating multimodal data and a knowledge graph.
• To evaluate the model's performance against traditional recommendation methods.

Main Methods:

• Utilized Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for audio and video feature extraction.
• Employed multi-layer perceptrons to encode user behavior data.
• Constructed a music domain knowledge graph and fused it with extracted features for enhanced semantic understanding.

Main Results:

• The proposed model achieved a Top-K accuracy of 0.90, significantly outperforming collaborative filtering and content-based methods.
• Demonstrated high accuracy and robustness when processing sparse datasets.
• Exhibited superior overall performance in key performance indicators for music recommendation.

Conclusions:

• The deep learning model with knowledge graph integration offers a significant advancement in personalized music learning recommendations.
• The platform provides reliable, efficient, and adaptable recommendation services.
• This approach effectively addresses challenges posed by data sparsity and enhances user experience.