解构可视化推荐中的分类：一种分类法与比较研究
Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study
AI总结：
本文构建可视化推荐类别分类法并开发 Frontier 系统，通过用户比较实验揭示增强当前视图与筛选子群体两类推荐最有用，为自适应个性化 VisRec 提供依据。
摘要：
可视化推荐（VisRec）系统在探索性数据分析过程中为用户提供关于潜在有趣且有用的后续步骤的建议。这些推荐通常根据其分析动作（即用于从当前探索状态转换到推荐可视化的操作）被组织成不同类别。然而，尽管近期工作中涌现了大量 VisRec 系统，但这些系统所采用类别在分析工作流中的效用尚未得到系统研究。本文通过形式化常见类别的分类法，并开发一个实现这些类别的系统 Frontier，来探索推荐类别的效能。借助 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，在数据探索过程中，通过添加属性来增强当前可视化的推荐，以及筛选到子群体的推荐相对最有用。我们的发现为通过精心选择的有效推荐类别实现自适应和个性化的下一代 VisRec 系统铺平了道路。
英文摘要：
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 paper 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
