Abstract 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 analyse 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 analyse 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 on different synthetic datasets and conclude with an exemplary analysis of a real-world dataset, examining the behavior of users interacting with voice assistants.
Keywords Behavior Analysis, Sequential Data Analysis, Autoregressive Language Models
Appendix can be found here: https://professor-x.de/deeptrails-arxiv
First install the requirements:
pip install -r requirements.txt
Then run the code:
Create dataset:
python train_gpt.py data
for the synthetic HypTrails dataset,
python train_gpt.py data-mixedtrails
for the synthetic MixedTrails dataset and
python train_gpt.py data-subtrails
for the synthetic SubTrails dataset.
Train model:
python train_gpt.py even
for the even model. You can replace even with any other hypothesis. For more detail, see train_gpt.py.
For random forest models, use train_rf.py instead.
We evaluate our methods using the notebooks in the Code/Evaluation folder.