计算机基础泛在学习资源推荐系统设计

Design of recommendation system for ubiquitous learning resources of Computer Foundation

采用RBF算法对传统的协同过滤算法进行优化改进,构建计算机基础泛在学习资源推荐系统。研究结果表明,RBF算法的迭代次数比BP算法少288次,耗费时间少约346s,说明RBF算法更适合于优化协同过滤模型;经RBF优化后的协同过滤算法在最近邻居数相同时均低于传统协同过滤算法;在推荐学习资源个数为5时,优化协同过滤推荐模型的推荐准确率达到82%。结果表明,泛在学习资源推荐系统能够较为准确地为计算机基础学习者推荐需要的学习资源,具有一定的实用性。

RBF algorithm is used to optimize and improve the traditional collaborative filtering algorithm,and a computer-based ubiquitous learning resource recommendation system is constructed.The results show that the MAE value of the optimized collaborative filtering model is 0.06 lower than that of the traditional collaborative filtering model when the number of nearest neighbors is 5;when the number of recommended learning resources is 5,the recommendation accuracy of the optimized collaborative filtering model reaches 82%;the questionnaire results show that 97.5%of the learners think that the ubiquitous learning resources recommendation system is effective.The above results show that the ubiquitous learning resources recommendation system can more accurately recommend the learning resources for computer-based learners,and has a certain practicality.