自适应过滤算法在基于社区E-learning的个性化知识服务系统中的研究

针对学习型社区中的教育需求,在传统算法上加以改进提出了一种基于向量空间模型的教育资源自适应过滤算法。首先通过训练算法,提取特征向量和伪反馈建立初始模板,设置初始阈值。然后通过过滤算法根据用户的反馈信息自适应地调整模板和阈值。该算法在执行过程中,不需要大量的初始文本,同时在过滤的过程中可不断地进行自主学习来提高过滤精度。该算法已在项目中进行验证,结果表明是有效的。

Personalized knowledge service system in community E-learning using an adaptive filtering algorithm

An adaptive filtering algorithm was developed to select for a personalized E-learning system in a learning community.The teaching resources are selected based on a vector space model.Feature selection and pseudo feedback are used to select the initial filtering profiles and thresholds through a training algorithm.Then user feedback is used to adaptively modify the profiles and thresholds through a filtering algorithm.The algorithm uses self-study to improve its accuracy and does not require massive initial texts in the filtering process.The algorithm effectively allocated resources in a personalized knowledge service system based on community E-learning.