  Visualization recommendation work has focused solely on scoring
visualizations based on the underlying dataset and not the actual user and
their past visualization feedback. These systems recommend the same
visualizations for every user, despite that the underlying user interests,
intent, and visualization preferences are likely to be fundamentally different,
yet vitally important. In this work, we formally introduce the problem of
personalized visualization recommendation and present a generic learning
framework for solving it. In particular, we focus on recommending
visualizations personalized for each individual user based on their past
visualization interactions (e.g., viewed, clicked, manually created) along with
the data from those visualizations. More importantly, the framework can learn
from visualizations relevant to other users, even if the visualizations are
generated from completely different datasets. Experiments demonstrate the
effectiveness of the approach as it leads to higher quality visualization
recommendations tailored to the specific user intent and preferences. To
support research on this new problem, we release our user-centric visualization
corpus consisting of 17.4k users exploring 94k datasets with 2.3 million
attributes and 32k user-generated visualizations.
