More Like Vis, Less Like Vis: Comparing Interactions for Integrating User Preferences Into Partial Specification Recommenders

Grace Guo, Subhajit Das, Jian Zhao, Alex Endert

Abstract: Visualization recommendation systems make data exploration less tedious by automating the process of visualization generation. They are particularly helpful for non-expert users who may not be familiar with a data set or the process of visualization specification. These systems allow users to input their preferences in the form of partial specifications to steer the recommendations made. However, the interaction approaches for partial specification input and their trade-offs have not been explored in prior work. In this article, we compare three different combinations of interaction approaches and granularities for users to indicate a preferred partial specification: 1) manual input, 2) inferring preferred partial specifications from binary like/dislike ratings for a visualization as a whole, or 3) inferring preferred partial specifications from binary like/dislike ratings for granular components of a visualization specification. In a between-subjects study, participants were assigned to one of three conditions and asked to complete a data exploration task. Our results indicate that manual input led to a greater coverage of data dimensions, while like/dislike ratings led to a greater diversity of marks and channels used. Qualitative participant feedback also reveals differences in user strategy and visualization comprehension across the three interaction conditions. Finally, we conclude with a discussion on implications for multiplicity and visualization comprehension during visual data exploration.

External IDs:dblp:journals/tvcg/Guo00E25