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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Visualization recommendation (VisRec) systems aid exploratory data analysis by suggesting next steps.
    • Recommendations are often categorized by analytical actions, but their utility is understudied.

    Purpose of the Study:

    • To systematically investigate the efficacy of VisRec categories in analytical workflows.
    • To formalize a taxonomy of common VisRec categories and evaluate their impact on user strategies.

    Main Methods:

    • Developed a system, Frontier, implementing a formalized taxonomy of VisRec categories.
    • Evaluated user workflow strategies and the influence of categories through user studies.

    Main Results:

    • Users found recommendations that add attributes to enhance visualizations most useful.
    • Recommendations that filter data to sub-populations were also highly valued.

    Conclusions:

    • The choice of recommendation categories significantly impacts user strategies in data exploration.
    • Effective categories are crucial for developing adaptive and personalized next-generation VisRec systems.