Zhang Songheng, Li Haotian, Qu Huamin, Wang Yong
IEEE Trans Vis Comput Graph. 2024 Sep;30(9):5923-5938. doi: 10.1109/TVCG.2023.3316469. Epub 2024 Jul 31.
Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable approach to recommend one or multiple appropriate visualizations for a tabular dataset. It leverages a box embedding-based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, dataset columns, datasets, and visualization choices). The embeddings of the entities and relations can be learned from dataset-visualization pairs. Also, AdaVis incorporates the attention mechanism into the inference framework. Attention can indicate the relative importance of data features for a dataset and provide fine-grained explainability. Our extensive evaluations through quantitative metric evaluations, case studies, and user interviews demonstrate the effectiveness of AdaVis.
自动化可视化推荐有助于快速创建有效的可视化，这对时间有限且数据可视化知识有限的用户尤其有益。利用机器学习（ML）技术实现端到端可视化推荐的趋势日益明显。然而，现有的基于ML的方法隐含地假设对于特定数据集只有一种合适的可视化，而这在实际应用中往往并非如此。此外，它们通常像一个黑箱一样工作，用户很难理解推荐特定可视化的原因。为了填补这一研究空白，我们提出了AdaVis，一种自适应且可解释的方法，用于为表格数据集推荐一个或多个合适的可视化。它利用基于盒嵌入的知识图谱来很好地建模不同实体（即数据特征、数据集列、数据集和可视化选择）之间可能的一对多映射关系。实体和关系的嵌入可以从数据集-可视化对中学习。此外，AdaVis将注意力机制纳入推理框架。注意力可以指示数据特征对于数据集的相对重要性，并提供细粒度的可解释性。我们通过定量指标评估、案例研究和用户访谈进行的广泛评估证明了AdaVis的有效性。
