基于三维特征协同支配的个性化学习资源推荐方法

摘要/Abstract

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。

关键词: 二进制粒子群优化算法, 个性化学习资源推荐, 隶属函数, 模糊控制

Abstract: 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.

Key words: Binary particle swarm optimization algorithm, Fuzzy control, Membership function, Personalized learning resource recommendation

中图分类号:

TP311

李浩君, 张征, 张鹏威. 基于三维特征协同支配的个性化学习资源推荐方法[J]. 计算机科学, 2019, 46(6A): 461-467. https://doi.org/

LI Hao-jun, ZHANG Zheng, ZHANG Peng-wei. Personalized Learning Resource Recommendation Method Based on Three-dimensionalFeature Cooperative Domination[J]. Computer Science, 2019, 46(6A): 461-467. https://doi.org/

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