  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.
