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

• URL: http://arxiv.org/abs/2009.12316v1
• Date: Fri, 25 Sep 2020 16:13:29 GMT
• Title: ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data
• Authors: Xin Qian, Ryan A. Rossi, Fan Du, Sungchul Kim, Eunyee Koh, Sana Malik, Tak Yeon Lee, Joel Chan
• Abstract summary: visualization recommendation seeks to generate, score, and recommend to users useful visualizations automatically. We propose the first end-to-end ML-based visualization recommendation system that takes as input a large corpus of datasets and visualizations. 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.
• Score: 44.90479301447387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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