Foundations and Trends® in Databases > Vol 14 > Issue 1

A Systematic Review of Visualization Recommendation Systems: Goals, Strategies, Interfaces, and Evaluations

By Zehua Zeng, University of Maryland, College Park, USA, zhzeng@terpmail.umd.edu | Leilani Battle, University of Washington, USA, leibatt@uw.edu

 
Suggested Citation
Zehua Zeng and Leilani Battle (2024), "A Systematic Review of Visualization Recommendation Systems: Goals, Strategies, Interfaces, and Evaluations", Foundations and Trends® in Databases: Vol. 14: No. 1, pp 1-71. http://dx.doi.org/10.1561/1900000088

Publication Date: 10 Sep 2024
© 2024 Z. Zeng and L. Battle
 
Subjects
Approximate and interactive query processing,  Data mining and OLAP,  Database design and tuning,  Design and evaluation,  Information visualization,  Perception and the user interface
 

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In this article:
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

Abstract

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. In this survey, we review how visualization recommendation systems have been designed over the last 25 years and classify 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, we also review how visualization recommendation systems are evaluated in the literature. Given these observations, we present several open challenges and promising directions for future work in designing effective visualization recommendation systems.

DOI:10.1561/1900000088

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ISBN: 978-1-63828-402-4
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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

A Systematic Review of Visualization Recommendation Systems: Goals, Strategies, Interfaces, and Evaluations

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.

 
DBS-088