Table of Contents
ISRN Artificial Intelligence
Volume 2012, Article ID 389540, 8 pages
Research Article

Personalized Recommendation in Interactive Visual Analysis of Stacked Graphs

1DHCJAC, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-877, Japan
2ISE, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-877, Japan

Received 14 October 2011; Accepted 2 November 2011

Academic Editors: J. A. Hernandez, W. Lam, A. Salmerón, and L. S. Wang

Copyright © 2012 Alejandro Toledo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.