Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations. In this paper, we present a pipeline of applying a large body of graphical perception results to develop new visualization recommendation algorithms and conduct an exploratory study to investigate how results from graphical perception can alter the behavior of downstream algorithms. Specifically, we model graphical perception results from 30 papers in Draco -- a framework to model visualization knowledge -- to develop new recommendation algorithms. By analyzing Draco-generated algorithms, we showcase the feasibility of our method to (1) identify gaps in existing graphical perception literature informing recommendation algorithms, (2) cluster papers by their preferred design rules and constraints, and (3) investigate why certain studies can dominate Draco's recommendations, whereas others may have little influence. Given our findings, we discuss the potential for mutually reinforcing advancements in graphical perception and visualization recommendation research.

翻译：来自图形感知的研究成果可以指导可视化推荐算法识别有效的可视化设计。然而，现有算法最多仅利用少量研究中的知识，限制了我们理解互补（或矛盾）的图形感知结果如何影响生成的推荐。本文提出一种将大量图形感知研究成果应用于开发新可视化推荐算法的流水线，并开展探索性研究以考察图形感知结果如何改变下游算法的行为。具体而言，我们将30篇论文中的图形感知结果建模到Draco（一个用于建模可视化知识的框架）中，以开发新的推荐算法。通过分析Draco生成的算法，我们展示了该方法在以下方面的可行性：（1）识别现有图形感知文献中告知推荐算法的知识空白，（2）根据优选设计规则和约束对论文进行聚类分析，以及（3）探究为何某些研究能主导Draco的推荐，而其他研究则影响甚微。基于我们的发现，我们探讨了图形感知与可视化推荐研究相互促进的潜力。