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.

本研究使用机器学习及概率模型检验等计算方法，以用户行为追踪数据为基础，无需任务或绝对度量，无监督地推断出移动应用中的交互样式，并通过重新设计和实现来优化其用户体验。