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

作　　者：沈云云[1] SHEN Yun-yun(School of information,Huaibei Normal University,Huaibei 235000,Anhui,China)

机构地区：[1]淮北师范大学信息学院,安徽淮北235000

出　　处：《贵阳学院学报（自然科学版）》2021年第3期5-8,共4页Journal of Guiyang University：Natural Sciences

基　　金：2020安徽省教育厅教学研究一般项目“‘互联网+’背景下泛在学习教学模式研究——以《大学计算机基础》课程为例”(项目编号:2020jyxm1703)。

摘　　要：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.

关 键 词：泛在学习 学习资源 协同过滤算法 RBF算法 

分 类 号：G434[文化科学—教育学]