Toward Understanding Representation Methods in Visualization Recommendations
Sehi L'Yi, Youli Chang , DongHwa Shin, and Jinwook Seo / 2019
- Sehi L'Yi, Seoul National University
- Youli Chang, Seoul National University
- DongHwa Shin, Seoul National University
- Jinwook Seo, Seoul National University
Most visualization recommendation systems predominantly rely on graphical previews to describe alternative visual encodings. However, since InfoVis novices are not familiar with visual representations (e.g., interpretation barriers), novices might have difficulty understanding and choosing recommended visual encodings. As an initial step toward understanding effective representation methods for visualization recommendations, we investigate the effectiveness of three representation methods (i.e., previews, animated transitions, and textual descriptions) under scatterplot construction tasks. Our results show how different representations individually and cooperatively help users understand and choose recommended visualizations, for example, by supporting their expect-and-confirm process. Based on our study results, we discuss design implications for visualization recommendation interfaces.