  More visualization systems are simplifying the data analysis process by
automatically suggesting relevant visualizations. However, little work has been
done to understand if users trust these automated recommendations. In this
paper, we present the results of a crowd-sourced study exploring preferences
and perceived quality of recommendations that have been positioned as either
human-curated or algorithmically generated. We observe that while participants
initially prefer human recommenders, their actions suggest an indifference for
recommendation source when evaluating visualization recommendations. The
relevance of presented information (e.g., the presence of certain data fields)
was the most critical factor, followed by a belief in the recommender's ability
to create accurate visualizations. Our findings suggest a general indifference
towards the provenance of recommendations, and point to idiosyncratic
definitions of visualization quality and trustworthiness that may not be
captured by simple measures. We suggest that recommendation systems should be
tailored to the information-foraging strategies of specific users.
