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AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data.

Songheng Zhang, Haotian Li, Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

|September 18, 2023

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AdaVis offers an adaptive and explainable machine learning (ML) approach for recommending visualizations. It addresses limitations of current ML methods by modeling one-to-many data-to-visualization relationships and providing feature importance insights.

Area of Science:

Computer Science
Data Visualization
Machine Learning

Background:

Automated visualization recommendation aids users with limited data visualization expertise and time.
Current machine learning (ML) approaches often assume a single best visualization, which is not always accurate.
Existing ML methods can be opaque, lacking transparency in their recommendation reasoning.

Purpose of the Study:

To develop an adaptive and explainable approach for recommending visualizations for tabular data.
To address the limitations of existing ML-based visualization recommendation systems.

Main Methods:

Proposed AdaVis, an adaptive and explainable approach utilizing a box embedding-based knowledge graph.
Modeled one-to-many relationships between data features, datasets, and visualization choices.

Related Experiment Videos

Incorporated an attention mechanism for feature importance and fine-grained explainability.

Main Results:

AdaVis effectively recommends one or multiple appropriate visualizations for tabular datasets.
The knowledge graph successfully models complex entity relationships.
The attention mechanism provides interpretable insights into feature relevance.

Conclusions:

AdaVis enhances visualization recommendation by offering adaptability and explainability.
The approach improves upon existing ML methods by handling multiple valid visualizations.
Evaluations confirm the effectiveness of AdaVis through quantitative metrics, case studies, and user feedback.