论文标题
用户建模技术的统一比较，用于预测数据交互和检测勘探偏差
A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias
论文作者
论文摘要
Visual Analytics Community提出了几种用户建模算法，以捕获和分析用户的交互行为，以帮助用户进行数据探索和洞察力生成。例如，有些人可以检测勘探偏见，而另一些人可以预测用户在进行交互之前将与用户进行交互的数据点。研究人员认为，这种算法收集可以帮助创建更智能的视觉分析工具。但是，社区缺乏对这些现有技术的严格评估和比较。结果，关于使用哪种方法以及何时使用的指导有限。我们的论文旨在通过比较和对八种用户建模算法进行比较并根据其在四个用户研究数据集的多样化的表现来填补这一缺失的空白。我们分析了探索偏差检测，数据相互作用预测和算法复杂性等措施。根据我们的发现，我们重点介绍了分析用户互动和可视化出处的开放挑战和新方向。
The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and insight generation. For example, some can detect exploration biases while others can predict data points that the user will interact with before that interaction occurs. Researchers believe this collection of algorithms can help create more intelligent visual analytics tools. However, the community lacks a rigorous evaluation and comparison of these existing techniques. As a result, there is limited guidance on which method to use and when. Our paper seeks to fill in this missing gap by comparing and ranking eight user modeling algorithms based on their performance on a diverse set of four user study datasets. We analyze exploration bias detection, data interaction prediction, and algorithmic complexity, among other measures. Based on our findings, we highlight open challenges and new directions for analyzing user interactions and visualization provenance.
