Competing Models: Inferring Exploration Patterns and Information Relevance via Bayesian Model Selection
Competing Models: Inferring Exploration Patterns and Information Relevance via Bayesian Model Selection
复制标题
竞争模型：通过贝叶斯模型选择推断探索模式和信息相关性
DOI：
10.1109/tvcg.2020.3030430
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发表时间：
2021
影响因子：
5.2
通讯作者：
Ottley, Alvitta
中科院分区：
文献类型：
--
作者：
Monadjemi, Shayan;Garnett, Roman;Ottley, Alvitta
分析交互数据提供了了解用户、揭示其潜在目标和创建智能可视化系统的机会。可视化中智能响应的第一步是使计算机能够通过观察用户与系统的交互来推断用户的目标和策略。研究人员提出了多种技术来建模用户，然而，他们的框架往往依赖于可视化设计、交互空间和数据集。由于这些依赖关系，许多技术不能为用户探索建模提供通用的算法解决方案。在本文中，我们基于数据集构建了一系列模型，并将用户探索建模作为贝叶斯模型选择问题，在该问题中，我们对可以解释用户交互的众多竞争模型保持信念。这些相互竞争的模型代表了用户在会话期间可以采用的一种探索策略。我们技术的目标是通过观察用户的低级交互，对用户进行高级和深入的推断。虽然我们提出的想法适用于各种概率模型空间，但我们展示了编码探索模式作为竞争模型来推断信息相关性的具体实例。我们验证了我们的技术能够推断探索偏差，预测未来的交互，并使用用户研究数据集总结分析会话。我们的结果表明，根据不同的应用，我们的方法在偏差检测和未来相互作用预测方面优于既定的基线。最后，我们讨论了基于我们提出的建模范式的未来研究方向，并建议从业者如何使用该方法构建智能可视化系统，以了解用户的目标并适应改进探索过程。
Analyzing interaction data provides an opportunity to learn about users, uncover their underlying goals, and create intelligent visualization systems. The first step for intelligent response in visualizations is to enable computers to infer user goals and strategies through observing their interactions with a system. Researchers have proposed multiple techniques to model users, however, their frameworks often depend on the visualization design, interaction space, and dataset. Due to these dependencies, many techniques do not provide a general algorithmic solution to user exploration modeling. In this paper, we construct a series of models based on the dataset and pose user exploration modeling as a Bayesian model selection problem where we maintain a belief over numerous competing models that could explain user interactions. Each of these competing models represent an exploration strategy the user could adopt during a session. The goal of our technique is to make high-level and in-depth inferences about the user by observing their low-level interactions. Although our proposed idea is applicable to various probabilistic model spaces, we demonstrate a specific instance of encoding exploration patterns as competing models to infer information relevance. We validate our technique's ability to infer exploration bias, predict future interactions, and summarize an analytic session using user study datasets. Our results indicate that depending on the application, our method outperforms established baselines for bias detection and future interaction prediction. Finally, we discuss future research directions based on our proposed modeling paradigm and suggest how practitioners can use this method to build intelligent visualization systems that understand users' goals and adapt to improve the exploration process.
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DOI：
10.1109/visual.2019.8933611
发表时间：
2019-10
期刊：
2019 IEEE Visualization Conference (VIS)
影响因子：
--
作者：
Emily Wall;J. Stasko;A. Endert
通讯作者：
Emily Wall;J. Stasko;A. Endert
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DOI：
10.1109/tvcg.2018.2865117
发表时间：
2019
影响因子：
5.2
作者：
Mi Feng;Evan M. Peck;Lane Harrison
通讯作者：
Lane Harrison
影响因子：
4
作者：
N. D. Rio;Paulo Pinheiro da Silva
通讯作者：
Paulo Pinheiro da Silva
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DOI：
10.1109/vast.2017.8585669
发表时间：
2017
期刊：
IEEE Visual Analytic Science and Technology (VAST
影响因子：
--
作者：
Wall, Emily;Blaha, Leslie M.;Franklin, Lyndsey;Endert, Alex
通讯作者：
Endert, Alex
DOI：
10.1109/vast.2009.5333020
发表时间：
2009
期刊：
2009 IEEE Symposium on Visual Analytics Science and Technology
影响因子：
--
作者：
Nazanin Kadivar;Victor Y. Chen;D. Dunsmuir;Eric Lee;Cheryl Z. Qian;J. Dill;C. Shaw;R. Woodbury
通讯作者：
R. Woodbury