  Visual representations of data (visualizations) are tools of great importance
and widespread use in data analytics as they provide users visual insight to
patterns in the observed data in a simple and effective way. However, since
visualizations tools are applied to sample data, there is a a risk of
visualizing random fluctuations in the sample rather than a true pattern in the
data. This problem is even more significant when visualization is used to
identify interesting patterns among many possible possibilities, or to identify
an interesting deviation in a pair of observations among many possible pairs,
as commonly done in visual recommendation systems.
  We present VizRec, a framework for improving the performance of visual
recommendation systems by quantifying the statistical significance of
recommended visualizations. The proposed methodology allows to control the
probability of misleading visual recommendations using both classical
statistical testing procedures and a novel application of the Vapnik
Chervonenkis (VC) dimension method which is a fundamental concept in
statistical learning theory.
