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.

翻译：可视化推荐系统（VRS）是一个新颖且具有挑战性的研究领域，其目标是从数据中自动生成富有洞察力的可视化结果，以支持非专业用户的信息发现过程。尽管在大数据时代具有巨大的应用潜力，该领域的研究进展却受到若干障碍的阻碍，其中包括缺乏用于训练推荐算法的标准化数据集，以及难以定义定量标准来评估生成图表的效果。本文旨在不仅总结VRS的最新研究进展，还勾勒出具有前景的未来研究方向。