Personalized Recommendation in Interactive Visual Analysis of Stacked Graphs

Abstract EN

We present a system which combines interactive visual analysis and recommender systems to support insight generation for the user.

Our approach combines a stacked graph visualization with a content-based recommender algorithm, where promising views can be revealed to the user for further investigation.

By exploiting both the current user navigational data and view properties, the system allows the user to focus on visual space in which she or he is interested.

After testing with more than 30 users, we analyze the results and show that accurate user profiles can be generated based on user behavior and view property data.