  Automated visualization recommendation facilitates the rapid creation of
effective visualizations, which is especially beneficial for users with limited
time and limited knowledge of data visualization. There is an increasing trend
in leveraging machine learning (ML) techniques to achieve an end-to-end
visualization recommendation. However, existing ML-based approaches implicitly
assume that there is only one appropriate visualization for a specific dataset,
which is often not true for real applications. Also, they often work like a
black box, and are difficult for users to understand the reasons for
recommending specific visualizations. To fill the research gap, we propose
AdaVis, an adaptive and explainable approach to recommend one or multiple
appropriate visualizations for a tabular dataset. It leverages a box
embedding-based knowledge graph to well model the possible one-to-many mapping
relations among different entities (i.e., data features, dataset columns,
datasets, and visualization choices). The embeddings of the entities and
relations can be learned from dataset-visualization pairs. Also, AdaVis
incorporates the attention mechanism into the inference framework. Attention
can indicate the relative importance of data features for a dataset and provide
fine-grained explainability. Our extensive evaluations through quantitative
metric evaluations, case studies, and user interviews demonstrate the
effectiveness of AdaVis.
