  The growing importance of data visualization in business intelligence and
data science emphasizes the need for tools that can efficiently generate
meaningful visualizations from large datasets. Existing tools fall into two
main categories: human-powered tools (e.g., Tableau and PowerBI), which require
intensive expert involvement, and AI-powered automated tools (e.g., Draco and
Table2Charts), which often fall short of guessing specific user needs. In this
paper, we aim to achieve the best of both worlds. Our key idea is to initially
auto-generate a set of high-quality visualizations to minimize manual effort,
then refine this process iteratively with user feedback to more closely align
with their needs. To this end, we present HAIChart, a reinforcement
learning-based framework designed to iteratively recommend good visualizations
for a given dataset by incorporating user feedback. Specifically, we propose a
Monte Carlo Graph Search-based visualization generation algorithm paired with a
composite reward function to efficiently explore the visualization space and
automatically generate good visualizations. We devise a visualization hints
mechanism to actively incorporate user feedback, thus progressively refining
the visualization generation module. We further prove that the top-k
visualization hints selection problem is NP-hard and design an efficient
algorithm. We conduct both quantitative evaluations and user studies, showing
that HAIChart significantly outperforms state-of-the-art human-powered tools
(21% better at Recall and 1.8 times faster) and AI-powered automatic tools
(25.1% and 14.9% better in terms of Hit@3 and R10@30, respectively).
