# VizML: A Machine Learning Approach to Visualization Recommendation

arXiv: https://arxiv.org/abs/1808.04819 · HTML: https://mlsanity.com/paper/1808.04819/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2018.

## Built on by (most cited first, 30 of 38)

- [Fine-tuning language models to find agreement among humans with diverse preferences](https://mlsanity.com/paper/2211.15006/) (2022)
- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task](https://mlsanity.com/paper/2112.12926/) (2021)
- [Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning](https://mlsanity.com/paper/2407.20174/) (2024)
- [Chart Question Answering: State of the Art and Future Directions](https://mlsanity.com/paper/2205.03966/) (2022)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [ParaView-MCP: An Autonomous Visualization Agent with Direct Tool Use](https://mlsanity.com/paper/2505.07064/) (2025)
- [EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation](https://mlsanity.com/paper/2112.07858/) (2021)
- [SimVecVis: A Dataset for Enhancing MLLMs in Visualization Understanding](https://mlsanity.com/paper/2506.21319/) (2025)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [AdaTyper: Adaptive Semantic Column Type Detection](https://mlsanity.com/paper/2311.13806/) (2023)
- [V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestions](https://mlsanity.com/paper/2406.15259/) (2024)
- [Infographics Wizard: Flexible Infographics Authoring and Design Exploration](https://mlsanity.com/paper/2204.09904/) (2022)
- [Generalization of CNNs on Relational Reasoning with Bar Charts](https://mlsanity.com/paper/2503.00086/) (2025)
- [Personalized Visualization Recommendation](https://mlsanity.com/paper/2102.06343/) (2021)
- [User-Centric Semi-Automated Infographics Authoring and Recommendation](https://mlsanity.com/paper/2108.11914/) (2021)
- [ChartKG: A Knowledge-Graph-Based Representation for Chart Images](https://mlsanity.com/paper/2410.09761/) (2024)
- [Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework](https://mlsanity.com/paper/2606.29808/) (2026)
- [Making Table Understanding Work in Practice](https://mlsanity.com/paper/2109.05173/) (2021)
- [VizAI : Selecting Accurate Visualizations of Numerical Data](https://mlsanity.com/paper/2111.04190/) (2021)
- [Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects](https://mlsanity.com/paper/2409.03805/) (2024)
- [Insight-centric Visualization Recommendation](https://mlsanity.com/paper/2103.11297/) (2021)
- [MitoVis: A Visually-guided Interactive Intelligent System for Neuronal Mitochondria Analysis](https://mlsanity.com/paper/2109.01351/) (2021)
- [Route Optimization via Environment-Aware Deep Network and Reinforcement Learning](https://mlsanity.com/paper/2111.09124/) (2021)
- [AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance](https://mlsanity.com/paper/2202.11928/) (2022)

## Abstract (arXiv)

Data visualization should be accessible for all analysts with data, not just the few with technical expertise. Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select, rather than manually specify. Here, we demonstrate a novel machine learning-based approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations. First, we identify five key design choices made by analysts while creating visualizations, such as selecting a visualization type and choosing to encode a column along the X- or Y-axis. We train models to predict these design choices using one million dataset-visualization pairs collected from a popular online visualization platform. Neural networks predict these design choices with high accuracy compared to baseline models. We report and interpret feature importances from one of these baseline models. To evaluate the generalizability and uncertainty of our approach, we benchmark with a crowdsourced test set, and show that the performance of our model is comparable to human performance when predicting consensus visualization type, and exceeds that of other ML-based systems.