为解决在线学习中出现的“认知过载”和“学习迷航”等问题, 针对用户的个性化学习需求, 同时考虑知识点之间的逻辑关系, 本文将知识图谱融入学习资源推荐模型. 首先构建了学科知识图谱、学习资源模型和用户数学模型, 综合考虑用户的兴趣偏好、用户知识库与学习资源所涵盖知识点的关联度以建立多目标优化模型. 然后使用自适应多目标粒子群算法对模型求解, 基于个体拥挤距离降序排列缩减外部种群规模, 获得了分布特征良好的两目标Pareto前沿, 输出推荐资源序列. 实验时通过与标准多目标粒子群算法对比并使用HV、IGD指标对模型进行评价, 验证了其多样性和稳定性, 证明了算法良好的全局寻优和收敛性能. 采用五折交叉验证了算法良好的推荐效用.
This study integrates a knowledge graph into a model for learning resource recommendation considering the logical relation between knowledge points, aiming to address the “cognitive overload” and “learning trek” in online learning and meet the users’ personalized learning needs. Firstly, a knowledge graph, a learning resource model, and a user-oriented mathematical model are developed. Then, we establish a multi-objective optimization model by taking into account the user preference and the correlation between the users’ knowledge base and the knowledge points covered by the learning resources. After that, this model is solved by the Adaptive Multi-Objective Particle Swarm Optimization (AMOPSO). Furthermore, we reduce the size of the external population through sorting the individual crowding distance in a descending order, thus obtaining the two-object Pareto frontier with optimal distribution and the recommended resource sequence. The proposed algorithm is also compared with the standard multi-objective particle swarm optimization and evaluated by HV and IGD, demonstrating its robust diversity, stability, global optimization, and convergence. Finally, five-fold cross-validation verifies the recommendation from the proposed algorithm.