A Systematic Review of Visualization Recommendation Systems: Goals, Strategies, Interfaces, and Evaluations
Author:   Zehua Zeng ,  Leilani Battle
Publisher:   now publishers Inc
ISBN:  

9781638284024


Pages:   88
Publication Date:   10 September 2024
Format:   Paperback
Availability:   In Print   Availability explained
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A Systematic Review of Visualization Recommendation Systems: Goals, Strategies, Interfaces, and Evaluations


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Overview

Visualization recommendation systems help data analysts navigate large, complex datasets by generating visualizations of meaningful patterns, outliers, and insights that could influence downstream decision-making. However, recommendations can easily mislead or confuse analysts when they are not developed with care. This monograph reviews how visualization recommendation systems have been designed over the last 25 years, and classifies them by their underlying recommendation goals and high-level implementation strategies, including the user interfaces provided for navigating and interpreting the recommended visualizations. To understand their efficacy, this work also reviews how visualization recommendation systems are evaluated in the literature. Given these observations, several open challenges and promising directions for future work in designing effective visualization recommendation systems are presented.

Full Product Details

Author:   Zehua Zeng ,  Leilani Battle
Publisher:   now publishers Inc
Imprint:   now publishers Inc
Weight:   0.137kg
ISBN:  

9781638284024


ISBN 10:   1638284024
Pages:   88
Publication Date:   10 September 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

1. Introduction 2. Background: Key Considerations in Designing Visualization Recommendation Systems 3. Common Architectures for Visualization Recommendation Systems 4. Inputs to and Outputs From Visualization Recommendation Algorithms 5. Learning Methods 6. Common User Interface Designs 7. Evaluation Methods 8. Open Challenges 9. Conclusion References

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