An Evaluation-Focused Framework for Visualization Recommendation Algorithms
An Evaluation-Focused Framework for Visualization Recommendation Algorithms
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DOI：
10.1109/tvcg.2021.3114814
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发表时间：
2021-09
影响因子：
5.2
通讯作者：
Zehua Zeng;Phoebe Moh;F. Du;J. Hoffswell;Tak Yeon Lee;Sana Malik;Eunyee Koh;L. Battle
中科院分区：
文献类型：
--
作者：
Zehua Zeng;Phoebe Moh;F. Du;J. Hoffswell;Tak Yeon Lee;Sana Malik;Eunyee Koh;L. Battle
尽管我们已经看到了推荐可视化的算法的扩散，但这些算法很少相互比较，因此很难确定哪种算法最适合给定的视觉分析方案。尽管已经提出了一些正式的框架，但我们认为这个问题仍然存在，因为从评估的角度来看，可视化建议算法是不充分指定的。在本文中，我们提出了一个以评估为重点的框架，以将各种可视化建议算法进行上下文化和比较。我们介绍了框架的结构，其中使用三个组件指定算法：（1）一个代表可能可视化设计的完整空间的图形，（2）用于遍历图形以获取潜在候选者的方法，以及（3）用于对候选设计进行排名的甲骨文。为了展示我们的框架如何指导算法性能的形式比较，我们不仅在理论上比较了五种现有的代表性建议算法，而且还根据理论比较的发现，从经验上比较了四种新算法。我们的结果表明，这些算法在用户性能方面的行为相似，强调了对建议算法进行更严格的正式比较，以进一步阐明其在各种分析方案中的好处。
Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.