DashBot：基于深度强化学习的洞察驱动仪表板生成
DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning
摘要：
分析仪表板在商业智能中很受欢迎，可以通过多个图表促进洞察力的发现。但是，创建一个有效的仪表板要求很高，这需要用户具有足够的数据分析背景，并熟悉专业工具，例如Power BI。要创建仪表板，用户必须通过选择数据列和探索不同的图表组合来配置图表，以优化见解的传达，这是一种试错。最近的研究已经开始使用深度学习方法来生成仪表板，以减轻可视化创建的负担。然而，由于缺乏大规模和高质量的仪表板数据集，这些努力受到了极大的阻碍。在这项工作中，我们建议使用深度强化学习来生成分析仪表板，这些仪表板可以使用完善的可视化知识和强化学习的估计能力。具体来说，我们使用可视化知识构建一个训练环境和奖励代理探索和模仿人类的探索行为与一个精心设计的代理网络。通过消融研究和用户研究证明了深度强化学习模型的有用性。总之，我们的工作为开发有效的基于ML的可视化算法提供了新的机会，而无需预先训练数据集。
英文摘要：
Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets.
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