Abstract

Information visualization significantly enhances human perception by graphically representing complex data sets. The variety of visualization designs makes it challenging to efficiently evaluate all possible designs catering to users’ preferences and characteristics. Most existing evaluation methods perform user studies to obtain multivariate qualitative responses from users via questionnaires and interviews. However, these methods cannot support online evaluation of designs, as they are often time-consuming. A statistical model is desired to predict users’ preferences on visualization designs based on non-interference measurements (i.e., wearable sensor signals). In this work, we propose a Multivariate Regression of Mixed Responses (MRMR) to facilitate quantitative evaluation of visualization designs. The proposed MRMR method is able to provide accurate model prediction with meaningful variable selection. A simulation study and a user study of evaluating visualization designs with 14 effective participants are conducted to illustrate the merits of the proposed model.

摘要

信息可视化通过以图形表示复杂的数据集，极大地增强了人们的感知能力。可视化设计的多样性使有效评估所有可能的设计以迎合用户的喜好和特征成为一项挑战。大多数现有的评估方法都可以进行用户研究，以通过问卷和访谈从用户那里获得多元的定性反应。但是，这些方法通常很耗时，因此无法支持在线评估设计。需要统计模型以基于无干扰测量（即，可穿戴传感器信号）来预测用户对可视化设计的偏好。在这项工作中，我们提出了混合响应的多元回归（MRMR），以促进可视化设计的定量评估。所提出的MRMR方法能够通过有意义的变量选择来提供准确的模型预测。进行了一个仿真研究和一个用户研究，评估了14位有效参与者的可视化设计，以说明所提出模型的优点。