An e-learning recommender system (RS) aims to generate personalized recommendations based on learner preferences and goals. The existing RSs in the e-learning domain still exhibit drawbacks due to its inability to consider the learner characteristics in the recommendation process. In this paper, we are dealing with the new user cold-start problem, which is a major drawback in e-learning content RSs. This problem can be mitigated by incorporating additional learner data in the recommendation process. This paper proposes an ontology-based (OB) content recommender system for addressing the new user cold-start problem. In the proposed recommendation model, ontology is used to model the learner and learning objects with their characteristics. Collaborative and content-based filtering techniques are used in the recommendation model to generate the top N recommendations based on learner ratings. Experiments were conducted to evaluate the performance and prediction accuracy of the proposed model in cold-start conditions using the evaluation metrics mean absolute error, precision and recall. The proposed model provides more reliable and personalized recommendations by making use of ontological domain knowledge.

电子学习推荐系统（RS）旨在根据学习者的喜好和目标生成个性化推荐。电子学习领域中的现有RS仍然存在缺点，因为它无法在推荐过程中考虑学习者的特征。在本文中，我们正在处理新的用户冷启动问题，这是电子学习内容RS的主要缺点。通过在推荐过程中合并其他学习者数据，可以缓解此问题。本文提出了一种基于本体的（OB）内容推荐系统，用于解决新用户的冷启动问题。在提出的推荐模型中，本体被用来对学习者和学习对象及其特征进行建模。推荐模型中使用基于内容的协作过滤技术来基于学习者评分生成前N个推荐。进行了实验，使用平均绝对误差，精度和召回率等评估指标，评估了该模型在冷启动条件下的性能和预测准确性。所提出的模型通过利用本体领域知识提供了更可靠和个性化的建议。