Constructing a Data Visualization Recommender System

TL;DR

This paper presents a step-by-step guide and a new question-based decision tree model for building accessible, versatile, and transparent data visualization recommender systems that integrate task and data characteristics.

Contribution

It introduces a comprehensive manual for constructing visualization recommenders and a novel model that improves clarity and extendability over existing solutions.

Findings

The new model achieves similar recommendation accuracy as existing solutions.

It is simpler, clearer, and more versatile than current models.

The guide facilitates easy development of visualization recommenders.

Abstract

Choosing a suitable visualization for data is a difficult task. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such as low accessibility and indecisiveness. In this study, we first define a step-by-step guide on how to build a data visualization recommender system. We then use this guide to create a model for a data visualization recommender system for non-experts that aims to resolve the issues of current solutions. The result is a question-based model that uses a decision tree and a data visualization classification hierarchy in order to recommend a visualization. Furthermore, it incorporates both task-driven and data characteristics-driven perspectives, whereas existing solutions seem to either convolute these or focus on one of the two exclusively. Based on testing against existing solutions, it is shown that the new model reaches similar results while being simpler, clearer, more versatile, extendable and transparent. The presented guide can be used as a manual for anyone building a data visualization recommender system. The resulting model can be applied in the development of new data visualization software or as part of a learning tool.