Machine Learning for Visualization Recommendation Systems: Open Challenges and Future Directions

Visualization Recommendation Systems (VRS) are a novel and challenging field of study, whose aim is to automatically generate insightful visualizations from data, to support non-expert users in the process of information discovery. Despite its enormous application potential in the era of big data, progress in this area of research is being held back by several obstacles among which are the absence of standardized datasets to train recommendation algorithms, and the difficulty in defining quantitative criteria to assess the effectiveness of the generated plots. In this paper, we aim not only to summarize the state-of-the-art of VRS, but also to outline promising future research directions.