摘要
针对现有个性化学习资源推荐方法存在推荐模型单一、速度较慢和匹配度不高等问题,文中提出基于阶段衍变双向自均衡的个性化学习资源推荐方法.首先构建基于阶段衍变双向自均衡的学习资源推荐模型,完善资源推荐特征参数化表示与适应度函数构建.然后采用基于进化状态判定的模糊自适应二进制粒子群优化算法求解模型.实验表明,相比采用经典算法的推荐方法,文中方法推荐的学习资源序列匹配度更高,推荐速度更快.
The existing personalized learning resource recommendation methods have problems of single model and low speed and matching degree. Therefore a personalized learning resource recommendation method based on stage evolution bidirectional self-balancing（EBPLRM-M） is proposed. Firstly, a personalized learning resource recommendation model is built based on stage evolution bidirectional self-balancing. The parameterized representations of resource recommendation features and fitness functions are improved. Then, a fuzzy adaptive binary particle swarm optimization algorithm based on evolutionary state determination is adopted to optimize the model. Finally, simulation results show that EBPLRM-M has higher matching degree and recommended speed compared with the recommendation methods adopting the classical algorithms.
作者
李浩君
张征
张鹏威
LI Haojun1;ZHANG Zheng1;ZHANG Pengwei1
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2018年第10期921-932,共12页
Pattern Recognition and Artificial Intelligence
基金
国家社会科学基金项目(No.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
阶段衍变
模糊控制
Personalized Learning Resource Recommendation
Binary Particle Swarm OptimizationAlgorithm
Stage Evolution
Fuzzy Control
