A fine-grained course session recommendation method based on knowledge point pruning

Course recommendation represents a significant research avenue within the educational domain. Presently, it predominantly employs collaborative filtering techniques to generate recommendations based on users' historical learning behaviors, such as their past rating information. However, course information encompasses a plethora of knowledge point information. Relying exclusively on historical behavior for recommendations leads to a constrained information scope and an inability to capture more granular user interests, thereby resulting in suboptimal interpretability. To address these challenges, this paper introduces a fine-grained course session recommendation approach grounded in knowledge point pruning, which refines the set of candidate knowledge points and enhances dialogue quality. Initially, a multitude of candidate knowledge points are identified leveraging the constraint properties inherent in the graph structure. Subsequently, by assessing the similarity between the set of candidate knowledge points and the learner's current preferred knowledge points, those knowledge points with a high degree of irrelevance to the learner's preferences are pruned. Ultimately, a deep residual Q-network is utilized to process the learner's feedback knowledge points, historical dialogues, and candidate knowledge points, yielding an action output for inquiry or recommendation. The proposed method has been validated on the MOOCcube dataset, with improvements observed in both HR and NDCG metrics. This approach effectively mitigates the issues of poor interpretability of recommendation outcomes in the realm of online course recommendation and the inability to dynamically capture learners' granular interests.