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Constructing a Data Visualization Recommender System

  • Petra KubernátováEmail author
  • Magda Friedjungová
  • Max van Duijn
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 862)

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.

Keywords

Data visualization Recommender system Non-expert users 

Notes

Acknowledgements

Research supported by SGS grant No. SGS17/210/OHK3/3T/18 and GACR grant No. GA18-18080S.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Petra Kubernátová
    • 1
    Email author
  • Magda Friedjungová
    • 2
  • Max van Duijn
    • 1
  1. 1.Leiden Institute of Advanced Computer ScienceLeiden UniversityLeidenThe Netherlands
  2. 2.Faculty of Information TechnologyCzech Technical University in PraguePragueCzech Republic

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