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    Area of Science:

    • Human-Computer Interaction
    • Information Visualization
    • Data Science

    Background:

    • Visualization recommendation systems automate data exploration, aiding non-expert users.
    • User preferences guide these systems via partial specifications.
    • Interaction approaches for partial specifications remain underexplored.

    Purpose of the Study:

    • Compare three interaction approaches for partial specification input in visualization recommendation systems.
    • Evaluate trade-offs between manual input, whole-visualization ratings, and component ratings.
    • Investigate impact on data exploration, visualization diversity, and user comprehension.

    Main Methods:

    • A between-subjects study comparing three conditions: manual input, whole-visualization like/dislike ratings, and component-level like/dislike ratings.
    • Participants completed a data exploration task using a visualization recommendation system.
    • Qualitative feedback was collected to understand user strategies and comprehension.

    Main Results:

    • Manual input resulted in greater coverage of data dimensions.
    • Like/dislike ratings (both whole and component) led to increased diversity in marks and channels used.
    • Qualitative data revealed distinct user strategies and comprehension levels across conditions.

    Conclusions:

    • Different interaction approaches offer distinct benefits for visualization recommendation systems.
    • Manual input is effective for comprehensive data exploration.
    • Rating-based approaches enhance visualization variety and may impact user strategy.
    • Findings inform the design of more effective interactive visualization tools.