Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study

TL;DR

This paper introduces a taxonomy of visualization recommendation categories, implements a system called Frontier to evaluate their effectiveness, and finds that attribute addition and filtering recommendations are most useful for data exploration.

Contribution

It formalizes a taxonomy of recommendation categories, develops the Frontier system to evaluate them, and provides insights into their impact on user workflows during data analysis.

Findings

Recommendations adding attributes are highly useful.

Filtering to sub-populations improves exploration.

Category influence affects workflow strategies.

Abstract

Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendations are typically organized into categories based on their analytical actions, i.e., operations employed to transition from the current exploration state to a recommended visualization. However, despite the emergence of a plethora of VisRec systems in recent work, the utility of the categories employed by these systems in analytical workflows has not been systematically investigated. Our paper explores the efficacy of recommendation categories by formalizing a taxonomy of common categories and developing a system, Frontier, that implements these categories. Using Frontier, we evaluate workflow strategies adopted by users and how categories influence those strategies. Participants found recommendations that add attributes to enhance the current visualization and recommendations that filter to sub-populations to be comparatively most useful during data exploration. Our findings pave the way for next-generation VisRec systems that are adaptive and personalized via carefully chosen, effective recommendation categories.