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LoyalTracker: Visualizing Loyalty Dynamics in Search Engines.

Conglei Shi, Yingcai Wu, Shixia Liu

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    This summary is machine-generated.

    LoyalTracker visualizes user log data to track search engine loyalty and switching behavior. This system helps understand user defection patterns at scale.

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    Area of Science:

    • Information Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Vast user log data offers opportunities to study search engine loyalty and defection.
    • Analyzing large-scale user behavior data presents significant analytical challenges.

    Purpose of the Study:

    • Introduce LoyalTracker, a visual analytics system for tracking user loyalty and switching behavior across multiple search engines.
    • Develop and evaluate novel visualization techniques for understanding complex user log data.

    Main Methods:

    • Developed LoyalTracker, a visual analytics system.
    • Proposed an interactive flow view visualization technique based on a flow metaphor.
    • Integrated density map and word cloud visualizations for deeper pattern analysis.
    • Conducted case studies and expert interviews for validation.

    Main Results:

    • The flow view effectively summarizes the dynamics of user loyalty for thousands of users over time.
    • Integrated visualizations provide analysts with tools to gain further insights into user behavior patterns.
    • Demonstrated the system's utility in understanding search engine loyalty and switching dynamics.

    Conclusions:

    • LoyalTracker offers a scalable solution for analyzing user loyalty and defection in search engines.
    • The proposed visualization techniques enhance the understanding of complex user behavior from large datasets.
    • The system is valuable for domain experts studying user engagement and market dynamics.