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Hybrid Filtering Recommendation in E-Learning Environment

机译：在线学习环境中的混合过滤建议

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Personalized recommendation in an e-learning system can actively introduce useful learning resources for learners. It is a ¿push¿ mechanism in contrast to the ¿pull¿ way like Web searching. At the same time it is also a very efficient way especially when users can not describe their needs exactly. This paper put forward an approach to recommend right learning resources for users with different learning needs by hybrid filtering method. Learning resources are organized by learning topics through text analysis. Users with similar learning interests are found out to form different common interest groups by user behavior tracing and recording. Then, two-level user profiles are built based on common interest group detection and text analysis. At last, learning resources are introduced to users according to user profiles by collaborative filtering and content-based filtering respectively. A time factor is also introduced into the building of user profiles, which makes user profiles adapt to user's interest shifting.
机译：电子学习系统中的个性化推荐可以为学习者积极引入有用的学习资源。与网络搜索之类的“拉”方式相反，它是一种“推”机制。同时，这也是一种非常有效的方法，尤其是当用户无法准确描述其需求时。提出了一种通过混合过滤为不同学习需求的用户推荐合适的学习资源的方法。学习资源是通过文本分析通过学习主题来组织的。通过用户行为跟踪和记录，发现具有相似学习兴趣的用户形成了不同的共同兴趣组。然后，基于共同兴趣组检测和文本分析来建立两级用户配置文件。最后，分别通过协作过滤和基于内容的过滤，根据用户配置文件将学习资源介绍给用户。时间因素也被引入到用户简档的构建中，这使得用户简档适应于用户的兴趣转移。

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