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Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks
Author(s) -
L'Yi Sehi,
Chang Youli,
Shin DongHwa,
Seo Jinwook
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13682
Subject(s) - visualization , computer science , representation (politics) , human–computer interaction , information visualization , visual analytics , process (computing) , interpretation (philosophy) , data visualization , information retrieval , data science , artificial intelligence , programming language , politics , political science , law
Abstract 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 [GTS10]), 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.