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.

翻译：我们提出了一项研究，采用基于机器学习推断混合隐马尔可夫模型与概率模型检验相结合的新型计算方法，以揭示移动应用中的交互风格。这些风格随后被用于指导重新设计，该设计经实施部署后，再利用相同方法进行分析。数据集为记录的用户轨迹，分别收集自两个版本的各六个月部署期间，涉及数千名用户，并按不同时间间隔进行分段。该方法不预设任务或绝对指标（如参与度度量），而是通过无监督聚类推断及概率时序逻辑分析来揭示风格。对于两个版本，用户在使用的第一周/第一月/第一个月内所采用的风格，与第二、三个月内的风格存在明显差异，这一结果超出了我们最初的预期。