As an important research topic in intelligent teaching systems, personalized recommendation services of learning resources can effectively solve the “information overload” problem and provide effective learning. However, the traditional learning resource recommendation technology mainly aims to improve recommendation accuracy and cannot effectively ensure the diversity and novelty of recommendation results. In this paper, the learning resource recommendation task is modelled with a multi-objective optimization problem. This paper proposes the Multi-Objective Evolutionary Algorithm-based online learning Resource Recommendation Model to balance the system’s accuracy, novelty, and diversity. The proposed model includes the following four steps: learning clustering, optimization goal setting individual representation, and genetic operator. According to the experimental results, this algorithm can improve the recommendation performance of online learning resources. Compared with the existing recommendation algorithms, more accurate, diverse, and novel learning resource recommendation results can be obtained with the proposed algorithm.

中文翻译：

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基于多目标优化的海量在线学习资源推荐


学习资源的个性化推荐服务作为智能教学系统的重要研究课题，可以有效解决“信息过载”问题，提供有效的学习。然而，传统的学习资源推荐技术主要以提高推荐准确率为目标，无法有效保证推荐结果的多样性和新颖性。本文利用多目标优化问题对学习资源推荐任务进行建模。本文提出基于多目标进化算法的在线学习资源推荐模型，以平衡系统的准确性、新颖性和多样性。所提出的模型包括以下四个步骤：学习聚类、优化目标设置个体表示和遗传算子。实验结果表明，该算法能够提高在线学习资源的推荐性能。与现有的推荐算法相比，该算法可以获得更准确、更多样化、更新颖的学习资源推荐结果。