VizGRank: A Context-Aware Visualization Recommendation Method Based on Inherent Relations Between Visualizations

Visualization recommendation systems measure the importance of visualizations to make suggestions. While considering each visualization individually may be enough to gauge its importance in specific scenarios, it ignores the relations between visualizations under a visual analysis context. This paper is to study a strategy via a more general method called VizGRank which models the relations between visualizations as a graph, then calculates the importance of visualizations by adopting a graph-based algorithm. In this model, the relations derived from the visual encoding of the visualizations and the underlying data schema are used for recommendation. Due to the lack of public benchmarks, the effectiveness of the model is evaluated on the synthetic results from an existing public benchmark IDEBench as a workaround. However, since the existing benchmark is specific and synthetic and does not reflect the realistic scenarios of visualization recommendation completely, a new benchmark for visualization recommendation is designed and constructed by collecting real public datasets. Extensive experiments on both the public benchmark and the new benchmark demonstrate that the VizGRank can better capture the relative importance of visualization and outperforms the existing state-of-the-art method.