融合学习者时序行为和认知水平的个性化学习资源推荐算法

个性化服务是构建智慧学习环境的内在要求和建设要点.为学习环境中的主体（学习者）推送个性化学习资源可以提高学习资源的利用概率，解决在线学习容易产生的迷航问题.通过本体知识库的统一性语义建立学习者和学习资源内部结构特征，设计出一个有效计算两者相关性的推荐算法.算法中引入时间衰减函数来描述学习者学习时的时序特征，导入计算学习者认知水平与学习资源难度的匹配算子以体现学习的循序渐进原则.实验结果表明：所构建的时间函数和匹配算子达到了预期目标，更好地提升了所推荐学习资源的质量和适应性，且算法的时间复杂度能满足实时计算要求.

Personalized Learning Resources Recommendation Algorithm Combining Learners' Time-Ordered Behaviors and Cognitive Level

Personalized service is the inherent requirement and key point in building an intelligent learning environment. The utilized probability of learning resources can be improved by pushing algorithm for main body (learner) of the learning environment, and then can solve the problem that learners easily lose when they are studying on-line. The internal structure characteristics of the learners and learning resources are established through the unity semantics based on knowledge ontology, then a recommendation algorithm which combines the time attenuation function and difficulty matching method is designed to effectively calculate the correlation between them. The time attenuation function expresses the time-ordered behaviors of the learners in order to reflect the knowledge migration feature, and the difficulty matching method matches with learners'' cognitive level and resource''s difficulty. Finally, experimental results show that the time attenuation function and difficulty matching method reach the expected target and can guarantee the quality of personalized learning resources recommendation better in their common effect.