  Analyzing, understanding, and describing human behavior is advantageous in
different settings, such as web browsing or traffic navigation. Understanding
human behavior naturally helps to improve and optimize the underlying
infrastructure or user interfaces. Typically, human navigation is represented
by sequences of transitions between states. Previous work suggests to use
hypotheses, representing different intuitions about the navigation to analyze
these transitions. To mathematically grasp this setting, first-order Markov
chains are used to capture the behavior, consequently allowing to apply
different kinds of graph comparisons, but comes with the inherent drawback of
losing information about higher-order dependencies within the sequences. To
this end, we propose to analyze entire sequences using autoregressive language
models, as they are traditionally used to model higher-order dependencies in
sequences. We show that our approach can be easily adapted to model different
settings introduced in previous work, namely HypTrails, MixedTrails and even
SubTrails, while at the same time bringing unique advantages: 1. Modeling
higher-order dependencies between state transitions, while 2. being able to
identify short comings in proposed hypotheses, and 3. naturally introducing a
unified approach to model all settings. To show the expressiveness of our
approach, we evaluate our approach on different synthetic datasets and conclude
with an exemplary analysis of a real-world dataset, examining the behavior of
users who interact with voice assistants.
