Historical views navigation though similarity and closeness centrality based recommendation

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.