用于揭示并解释交互风格的概率形式化建模
Probabilistic Formal Modelling to Uncover and Interpret Interaction Styles
AI总结：
本研究结合混合隐马尔可夫模型推断与概率模型检验，无监督揭示移动应用用户交互风格，用于指导应用重设计，且发现新旧版本用户使用初期与中后期风格均存在意外的显著差异。
摘要：
我们提出了一项采用新型计算方法的研究，该方法基于用于推断混合隐马尔可夫模型（admixture hidden Markov models）的机器学习与概率模型检验的全新组合，旨在揭示一款移动应用中的交互风格。这些风格随后被用于指导应用的重新设计，我们实现并部署了新版本，再用相同方法对其展开分析。研究数据集为记录的用户行为轨迹，来自两个版本各为期六个月的部署阶段，涉及数千名用户，并被划分为不同的时间区间。这些方法不对任务或参与度等绝对指标做预设，而是通过无监督聚类推断与概率时序逻辑分析来揭示交互风格。在两个版本中，用户在使用首日/首周/首月采用的风格，与使用第二个月和第三个月时的风格存在明显差异，这是我们未曾预料到的结果。
英文摘要：
We present a study using new computational methods, based on a novel combination of machine learning for inferring admixture hidden Markov models and probabilistic model checking, to uncover interaction styles in a mobile app. These styles are then used to inform a redesign, which is implemented, deployed, and then analysed using the same methods. The data sets are logged user traces, collected over two six-month deployments of each version, involving thousands of users and segmented into different time intervals. The methods do not assume tasks or absolute metrics such as measures of engagement, but uncover the styles through unsupervised inference of clusters and analysis with probabilistic temporal logic. For both versions there was a clear distinction between the styles adopted by users during the first day/week/month of usage, and during the second and third months, a result we had not anticipated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
