基于多目标优化策略的在线学习资源推荐方法

目前在线学习资源推荐较多采用单目标转化方法,推荐过程中对学习者偏好考虑相对不足,影响学习资源推荐精度.针对上述问题,文中提出基于多目标优化策略的在线学习资源推荐模型(MOSRAM),在学习者规划时间内,以同时获得学习者对学习资源类型偏好度最大和难度水平适应度最佳为优化目标,设计具有向邻居均值学习能力和探索新区域能力的多目标粒子群优化算法(NEMOPSO),提出以MOSRAM为核心的在线学习资源推荐方法(NEMOPSO-RA).不同问题规模下融合经典多目标优化算法的推荐方法对比实验表明,NEMOPSO-RA可以有效提高在线学习资源的推荐精度和推荐性能.

Method of Online Learning Resource Recommendation Based on Multi-objective Optimization Strategy

Single-objective transformation method is commonly used in online learning resource recommendation. In the recommendation process, the consideration of learner preference is inadequate. Therefore, the accuracy of learning resource recommendation is affected. An online learning resource recommendation model, multi-objective resource recommendation model(MOSRAM), is proposed based on multi-objective optimization strategy. In this model, learner preference for the type of learning resources and the fitness of the difficulty level are regarded as the optimization objectives in the planning time. A multi-objective particle swarm optimization algorithm, neighborhood multi-objective particle swarm optimization(NEMOPSO), with the ability to benefit from neighbor mean and explore new regions is designed. An online learning resource recommendation method, neighborhood multi-objective particle swarm optimization-resource recommendation approach(NEMOPSO-RA), based on MOSRAM model is proposed. The comparison of recommendation methods with classical multi-objective optimization algorithms under different problem scales show that the accuracy and performance of online learning resource recommendation can be effectively improved by NEMOPSO-RA method.