RHCR: a reinforced heterogeneous knowledge graph for course recommendation.

Area of Science:

• Artificial Intelligence
• Educational Technology
• Data Science

Background:

• Personalized learning and online education face challenges in optimizing learning paths for better outcomes and engagement.
• Traditional recommendation systems struggle with data sparsity and lack of interpretability, limiting personalized recommendations.

Purpose of the Study:

• To propose a novel course recommendation model, Reinforced Heterogeneous Knowledge Graph Reasoning for Course Recommendation (RHCR), to address limitations in personalized learning.
• To improve learning outcomes and learner engagement through optimized recommendation paths.

Main Methods:

• Introduced a heterogeneous course knowledge graph to mitigate sparse data and weak interactions.
• Formulated course path reasoning as a Markov Decision Process (MDP).
• Utilized the Asynchronous Advantage Actor-Critic (A3C) algorithm with Multi-Head Attention and Bidirectional Long Short-Term Memory (MHA-BiLSTM) for path optimization.

Main Results:

• RHCR demonstrated significant improvements, increasing Normalized Discounted Cumulative Gain (NDCG) by 8.16% and Precision by 16.29%.
• The model outperformed traditional neural network-based recommendation methods.
• RHCR effectively alleviated data sparsity and enhanced recommendation interpretability.

Conclusions:

• RHCR offers an effective solution for optimizing personalized learning paths in online education.
• The proposed model enhances learner engagement and improves overall learning outcomes.
• RHCR advances the field of personalized learning by providing a more interpretable and data-efficient recommendation system.