A Context-Aware Intelligent Educational Recommender System Incorporating User Behavioural Knowledge Graph and Causal Inference

With the advent of the digital age, the importance of personalized recommendation systems in the field of education has become increasingly prominent, especially in addressing the issues of information overload and difficulty in selecting learning resources. This study proposes a Context-Aware Intelligent Education Recommendation System (SAIERec), which integrates user behavior analysis, knowledge graph reasoning, and causal inference techniques to provide learners with accurate personalized learning resource recommendations. The system construction is based on an in-depth theoretical foundation, including context-aware recommendations and personalized recommendation research based on in-depth knowledge reasoning.By introducing deep learning technology, the system enhances its perception of complex educational contexts and uses knowledge graphs to improve the interpretability of recommendation results. The key construction path of the system covers the initialization of heterogeneous information networks of user behavior, semantic extension of situational knowledge in knowledge graphs, optimization of recommendation algorithms, enrichment of learning resources, and dynamic adjustment of personalized learning paths. In addition, the system uses an attention-based graph convolutional neural network for deep information mining and improves the accuracy and fairness of the recommendation system through counterfactual causal inference strategies. Experimental results show that the SAIERec system outperforms existing recommendation models on multiple datasets, verifying its effectiveness and application potential in the field of education.