In recent years, online learning has become more and more popular. However, because of information overload, learners often find it difficult to retrieve suitable learning resources. Although many scholars have proposed excellent online learning resource recommendation algorithms, the accuracy of personalized recommendation results still needs to be improved. This study proposes an improved hybrid ontology-based approach for online learning resource recommendations, combining collaborative filtering algorithm and sequential pattern mining (SPM) techniques. Ontology can be used effectively for knowledge representation to avoid cold start and data sparsity problems. And the history of learners’ sequential access patterns helps in providing recommendations that are more consistent with the law of learning activities. Experimental results reveal that our improved hybrid approach for learning resource recommendations yields better performance and recommendation quality than other related algorithms. Compared with previous research outcomes, our collaborative filtering engine, with ontology domain knowledge, makes full use of the historical learning paths of similar learners. The ontology construction in this study has a more reliable theoretical basis and the selection of features is more representative. In addition, improvement of the SPM process further improves the efficiency of our recommended algorithm.

近年来，在线学习变得越来越流行。然而，由于信息过载，学习者往往难以检索到合适的学习资源。虽然很多学者提出了优秀的在线学习资源推荐算法，但个性化推荐结果的准确性仍有待提高。本研究提出了一种改进的基于混合本体的在线学习资源推荐方法，结合了协同过滤算法和序列模式挖掘 (SPM) 技术。本体可以有效地用于知识表示，避免冷启动和数据稀疏问题。学习者顺序访问模式的历史有助于提供更符合学习活动规律的建议。实验结果表明，我们改进的学习资源推荐混合方法比其他相关算法产生更好的性能和推荐质量。与以往的研究成果相比，我们的协同过滤引擎具有本体领域知识，充分利用了相似学习者的历史学习路径。本研究的本体构建具有更可靠的理论基础，特征选择更具代表性。此外，SPM 过程的改进进一步提高了我们推荐算法的效率。充分利用同类学习者的历史学习路径。本研究的本体构建具有更可靠的理论基础，特征选择更具代表性。此外，SPM 过程的改进进一步提高了我们推荐算法的效率。充分利用同类学习者的历史学习路径。本研究的本体构建具有更可靠的理论基础，特征选择更具代表性。此外，SPM 过程的改进进一步提高了我们推荐算法的效率。