VizML: A Machine Learning Approach to Visualization Recommendation
Name
1808.04819.pdf
Description
Submitted version
Size
3.74 MB
Format
Unknown
Checksum (MD5)
81057d481555208d2949b3ce13b48696
Author(s)
Hu, Kevin
Bakker, Michiel A
Li, Stephen
Kraska, Tim
Hidalgo, Cesar Augusto
Date Issued
2019
Journal
Conference on Human Factors in Computing Systems - Proceedings
Publisher
Association for Computing Machinery (ACM)
Version
Original manuscript
Abstract
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. 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 visualization recommender systems.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3290605.3300358
