摘要
个性化推荐正成为信息服务时代的重要形式,是缓解学习者知识迷航、提升学习效率的有效途径。为了满足学习者对在线学习资源的个性化需求,提出一种基于三维特征协同支配的个性化学习资源推荐方法(TPLRM)。首先通过完善学习者与在线学习资源特征的匹配关系,建立了三维特征协同支配的个性化学习资源推荐模型,并进行参数化描述;其次设计了一种基于高斯隶属函数模糊控制的二进制粒子群优化算法(FCBPSO)来对推荐模型目标函数进行求解;最后在多个评价指标下,通过5组对比实验验证了TPLRM推荐方法有较好的推荐性能。
Personalized recommendation is becoming an important form of information service era,and it is an effective way to alleviate knowledge disorientation and improve learning efficiency.In order to meeting learners’personalized needs for online learning resources,personalized recommendation technology is increasingly important.Therefore,this paper proposed a personalized learning resource recommendation method based on three-dimensional feature cooperative domination(TPLRM).Firstly,a personalized learning resource recommendation model based on three-dimensional feature cooperative domination is constructed,resource recommendation feature parameters are improved,and fitness function is built.Secondly,the binary particle swarm optimization algorithm based on fuzzy control of Gauss’s membership function(FCBPSO)is used to solve the model.Finally,the evaluation target system is established.Five groups of comparative experiments verifies that TPLRM recommendation method has better recommendation performance.
作者
李浩君
张征
张鹏威
LI Hao-jun;ZHANG Zheng;ZHANG Peng-wei(College of Education Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《计算机科学》
CSCD
北大核心
2019年第B06期461-467,共7页
Computer Science
基金
国家自然科学基金项目(61503340)
国家社会科学基金项目(16BTQ084)资助
关键词
个性化学习资源推荐
二进制粒子群优化算法
模糊控制
隶属函数
Personalized learning resource recommendation
Binary particle swarm optimization algorithm
Fuzzy control
Membership function
