Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study
Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study
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DOI：
10.1109/tvcg.2021.3085751
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发表时间：
2021-02
影响因子：
5.2
通讯作者：
D. Lee;V. Setlur;Melanie Tory;Karrie Karahalios;Aditya G. Parameswaran
中科院分区：
文献类型：
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作者：
D. Lee;V. Setlur;Melanie Tory;Karrie Karahalios;Aditya G. Parameswaran
可视化建议（VISREC）系统为用户提供了有关在探索性数据分析过程中潜在有趣且有用的下一步的建议。这些建议通常根据其分析作用（即从当前的勘探状态过渡到建议的可视化的运算）组织成类别。但是，尽管在最近的工作中出现了众多Visrec系统，但这些系统在分析工作流程中采用的类别的实用性尚未系统地研究。我们的文章通过形式化共同类别的分类法并开发实现这些类别的系统，探讨推荐类别的功效。使用Frontier，我们评估用户采用的工作流策略以及类别如何影响这些策略。参与者发现了添加属性的建议，以增强当前的可视化和对亚群的过滤的建议，在数据探索过程中相对较大。我们的发现为通过精心选择的有效推荐类别进行自适应和个性化的下一代Visrec系统铺平了道路。
Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendations are typically organized into categories based on their analytical actions, i.e., operations employed to transition from the current exploration state to a recommended visualization. However, despite the emergence of a plethora of VisRec systems in recent work, the utility of the categories employed by these systems in analytical workflows has not been systematically investigated. Our article explores the efficacy of recommendation categories by formalizing a taxonomy of common categories and developing a system, Frontier, that implements these categories. Using Frontier, we evaluate workflow strategies adopted by users and how categories influence those strategies. Participants found recommendations that add attributes to enhance the current visualization and recommendations that filter to sub-populations to be comparatively most useful during data exploration. Our findings pave the way for next-generation VisRec systems that are adaptive and personalized via carefully chosen, effective recommendation categories.