  General visualization recommendation systems typically make design decisions
for the dataset automatically. However, most of them can only prune meaningless
visualizations but fail to recommend targeted results. This paper contributes
TaskVis, a task-oriented visualization recommendation system that allows users
to select their tasks precisely on the interface. We first summarize a task
base with 18 classical analytic tasks by a survey both in academia and
industry. On this basis, we maintain a rule base, which extends empirical
wisdom with our targeted modeling of the analytic tasks. Then, our rule-based
approach enumerates all the candidate visualizations through answer set
programming. After that, the generated charts can be ranked by four ranking
schemes. Furthermore, we introduce a task-based combination recommendation
strategy, leveraging a set of visualizations to give a brief view of the
dataset collaboratively. Finally, we evaluate TaskVis through a series of use
cases and a user study.

Human-Computer Interaction