# ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data

arXiv: https://arxiv.org/abs/2009.12316 · HTML: https://mlsanity.com/paper/2009.12316/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [Personalized Visualization Recommendation](https://mlsanity.com/paper/2102.06343/) (2021)
- [Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes](https://mlsanity.com/paper/2111.14674/) (2021)
- [Insight-centric Visualization Recommendation](https://mlsanity.com/paper/2103.11297/) (2021)
- [A Design Space for Explainable Ranking and Ranking Models](https://mlsanity.com/paper/2205.15305/) (2022)

## Abstract (arXiv)

Visualization recommendation seeks to generate, score, and recommend to users useful visualizations automatically, and are fundamentally important for exploring and gaining insights into a new or existing dataset quickly. In this work, we propose the first end-to-end ML-based visualization recommendation system that takes as input a large corpus of datasets and visualizations, learns a model based on this data. Then, given a new unseen dataset from an arbitrary user, the model automatically generates visualizations for that new dataset, derive scores for the visualizations, and output a list of recommended visualizations to the user ordered by effectiveness. We also describe an evaluation framework to quantitatively evaluate visualization recommendation models learned from a large corpus of visualizations and datasets. Through quantitative experiments, a user study, and qualitative analysis, we show that our end-to-end ML-based system recommends more effective and useful visualizations compared to existing state-of-the-art rule-based systems. Finally, we observed a strong preference by the human experts in our user study towards the visualizations recommended by our ML-based system as opposed to the rule-based system (5.92 from a 7-point Likert scale compared to only 3.45).