通过数据和视觉设计探针,从用户那里引发模型方向互动
IEEE transactions on visualization and computer graphics
|October 16, 2023
概括
本研究介绍了数据和视觉设计探测器,以帮助用户在交互式机器学习 (IML) 系统中引导机器学习模型. 研究结果揭示了用户如何与视觉编码进行交互,以指导ML模型行为.
科学领域:
- 人与计算机的交互 (HCI)
- 机器学习 (ML) 是指机器学习.
- 数据可视化 数据可视化
背景情况:
- 交互式机器学习 (IML) 系统越来越普遍,使各种用户能够分析数据.
- 在IML的一个关键挑战是将用户交互与视觉元素对齐,以有效地引导机器学习模型.
- 现有的用户引导的ML模型转向方法需要进一步改进以实现直观的交互设计.
研究的目的:
- 探索使用数据和视觉设计探测器来获取IML接口内引导ML模型中的用户意图.
- 了解用户交互模式与模型控制的视觉编码.
- 在IML系统中识别影响用户交互选择的因素,包括不交互.
主要方法:
- 进行了一项诱导研究,涉及20名具有不同机器学习专业知识的数据分析师.
- 采用数据和视觉设计探测器来收集用户对操纵机器学习模型的偏好.
- 用户与视觉编码的交互被系统地记录和分析.
主要成果:
- 结果被总结为目标交互对,提供了对所需用户指导行动的见解.
- 探测器的实用性通过将引起的相互作用与先前系统的相互作用进行比较来评估.
- 确定了影响用户与视觉编码的关键因素,包括避免互动的原因.
结论:
- 数据和视觉设计探测器是了解IML用户意图的宝贵工具.
- 通过这些探测器收集的经验证据可以为开发更有效的IML原型提供信息.
- 了解用户交互动态对于设计直观而强大的IML系统至关重要.
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