Learning Resource Recommendation Model Based on Collaborative Knowledge Graph Attention Networks

Aiming at the issue that most of the existing knowledge graph-based methods for personalized learning resource recommendations do not take full advantage of collaborative signals from learner interaction data, we introduce a novel model named Collaborative Knowledge Graph Attention Network-based Learning Resource Recommendation Model (CKALR). Firstly, instructional resources attribute is utilized to structure a knowledge graph, then naturally combines the explicit collaborative signals from learner-learning resource interactions with the auxiliary knowledge associations in the graph. At the same time, an attention method is employed to accurately obtain the individual preference implied in the learner's past interaction information, thereby further enriching the feature representations of both learner and the learning resources. Finally, we compute the inner product of the representations to estimate the user's preference for a given learning resource. The experiments are performed using the publicly available learning resource datasets, MOOCCube and Book-Crossing, and evaluated using metrics AUC, F1 score and Top-K evaluation metrics. The results show notable enhancements in both accuracy and interpretability when compared to other benchmark algorithms.