协同过滤在知识推荐中的应用

Application of collaborative filter in knowledge recommendation

将电子商务中的协同过滤推荐算法改进之后应用到知识推荐系统中,通过计算学习者的学习目标、学习背景和认知能力等信息的相似性进行用户聚类,然后使用协同过滤算法对相同聚类簇内的用户进行学习资源的推荐.实验结果表明,该方法可提高推荐的准确度和推荐效率,增强学习者满意度;传统协同过滤推荐中的新用户问题、实时性问题在知识推荐过程中也同样存在,具体解决方法将作为未来研究的主要内容.

To introduce modified collaborative filtering recommendation algorithm to knowledge recommendation system,the user cluster was carried out by calculating the similarity of learning objectives,background and cognitive competence between the different learners and then completing recommendation using collaborative filtering algorithm in the user cluster. The results showed that this method could improve the accuracy and efficiency of recommendation,and could meet user＇s meeds. In the process of knowledge recommendation,it also exits new user problem,real-time problem similar to traditional collaborative filtering recommendation. The specific solution method will be the main content of future research.