Using Cognitive Interest Graph and Knowledge-activated Attention for Learning Resource Recommendation

Abstract

Recently, a number of deep-learning based approaches have proved effective for personalized learning resource recommendation (PLR), but they ignore the impact of complex transitions in a learner’s interests and their cognitive ability. This undermines the accomplishment of personalized learning. This paper proposes a new method for learning session-based recommendation which uses a graph neural network (GNNs) and a cognitive interest graph that, for brevity, is referred to as CIGNN. It models the click sequence and exercise sequence in a learning session separately as a session graph and a cognitive graph, then creates a composite cognitive interest graph. CIGNN also uses a novel knowledge-activated attention mechanism that makes uses of the knowledge mastery of learners and their past behavior to actively adapt to their learning interests, so that representations of their preferences change as they acquire knowledge. A further novel feature of CIGNN is its use of an interest-aware semantic graph attention network to extract semantic information regarding different types of learning interests by drawing on meta-paths of different importance. Extensive experiments were conducted using real-world data and the results show that CIGNN can outperform existing baseline approaches for PLR-related tasks. We also provide some case studies that illustrate how CIGNN can help learners to master knowledge.