Intent-Aware Data Visualization Recommendation

Abstract

This paper proposes a visualization recommender system for tabular data given visualization intents (e.g., “population trends in Italy” and “smartphone market share”). The proposed method predicts the most suitable visualization type (e.g., line, pie, or bar chart) and visualized columns (columns used for visualization) based on statistical features extracted from the tabular data as well as semantic features derived from the visualization intent. To predict the appropriate visualization type, we propose a bi-directional attention (BiDA) model that identifies important table columns using the visualization intent and important parts of the intent using the table headers. To determine the visualized columns, we employ a pre-trained neural language model to encode both visualization intents and table columns and predict which columns are the most likely to be used for visualization. Since there was no available dataset for this task, we created a new dataset consisting of over 100 K tables and their appropriate visualization. Experiments revealed that our proposed methods accurately predicted suitable visualization types and visualized columns.