摘要
研究学习者学习兴趣,并根据学习者兴趣相似性的方法获得目标学习者的最近邻居集合,可更好地为学习者提供自适应学习服务并产生相应学习推荐。首先从学习者的兴趣度入手,采用相似性度量方法计算目标学习者的最近邻居;最后依据邻居学习者的兴趣度来预测目标学习者对学习页面的兴趣度,并产生推荐。实验结果表明,基于学习者兴趣相似性的推荐方法,提高了最近邻居计算的准确性和推荐质量。
Study on learners’learning interest and acquisition of target users’nearest neighbor set based on learners’interest similarity provide learners with self-adapted learning service and corresponding recommendation.From learners’leaning interestingness and by similarity calculation method the target users’nearest neighbor was calculated.Based on page access behavior,the page interestingness of target users was predicted and recommendation was given.The experimental results show that the application of the learners’interestingness similarity improves calculation accuracy of nearest neighbors and commenda-tion efficiency.
出处
《河北北方学院学报（自然科学版）》
2013年第5期27-30,共4页
Journal of Hebei North University:Natural Science Edition
基金
安徽科技学院教研项目:应用型本科院校计算机基础课程分类分级教学体系的构建(X2012088)
关键词
学习者兴趣度
相似性
推荐算法
兴趣度矩阵
learners' interestingness
similarity
recommendation algorithm
interestingness matrix
