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首页> 外文会议>International Symposium on Information Technology in Medicine and Education >Historical views navigation though similarity and closeness centrality based recommendation
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Historical views navigation though similarity and closeness centrality based recommendation

机译：历史观点导航虽然相似性和密闭中心的建议

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摘要

when faculty in university visualize the students' personal information, they usually focus on the current view, losing trace of the historical views, which results in missing of some important information or patterns. To address this problem and figure out the unknowns hidden in educational datasets, this paper proposes the historical views navigation though similarity and closeness centrality based recommendation. In this approach, the useful intermediate views or the views interested by users are saved as history and compared with the current view. By analyzing the similarity between them and the closeness centrality measure, the most relative historical views are recommended to the user. Finally, the user study shows that most of the participants are interested in our work. They think it's helpful and will continue to use it.
机译：当大学教师可视化学生的个人信息时，他们通常专注于当前视图，失去历史观点的迹象，这导致一些重要信息或模式。为了解决这个问题并弄清楚隐藏在教育数据集中的未知数，本文提出了历史观点，尽管相似性和亲密关系的建议。在这种方法中，有用的中间视图或感兴趣的用户的观点被保存为历史记录并与当前视图进行比较。通过分析它们之间的相似性和接近中心度量，向用户推荐最相关的历史视图。最后，用户学习表明，大多数参与者都对我们的工作感兴趣。他们认为这是有帮助的，并将继续使用它。

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