首页> 外文会议>International Conference on Technologies for E-Learning and Digtal Entertainment(Edutainment 2007); 20070611-13; Hong Kong(CN) >Research on Personalized Community E-Learning Recommendation Service System by Using Improved Adaptive Filtering Algorithm
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Research on Personalized Community E-Learning Recommendation Service System by Using Improved Adaptive Filtering Algorithm

机译:改进的自适应过滤算法研究个性化社区电子学习推荐服务系统

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摘要

To meet the needs of education in the learning community, an improved adaptive filtering algorithm for teaching resources based on vector space model was proposed in the paper. First, feature selection and pseudo feedback were used to select the initial filtering profiles and thresholds through training algorithm. Then user feedback was utilized to modify the profiles and thresholds adaptively through filtering algorithm. The algorithm had two advantages, the first was that it could carry on self-study to improve the precision; the second was that the execution did not need massive initial texts in the process of filtering. The algorithm was also used in personalized Recommendation service system based on Community E-learning. The result manifested that the algorithm was effective.
机译:为满足学习社区教育的需求,提出了一种改进的基于向量空间模型的教学资源自适应过滤算法。首先,特征选择和伪反馈用于通过训练算法选择初始过滤轮廓和阈值。然后利用用户反馈通过过滤算法自适应地修改配置文件和阈值。该算法具有两个优点,其一是可以进行自学习以提高精度。第二个原因是执行过程在筛选过程中不需要大量的初始文本。该算法还被用于基于社区电子学习的个性化推荐服务系统。结果表明该算法是有效的。

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