Higher-Order DeepTrails: Unified Approach to *Trails
Tobias Koopmann, Jan Pfister, Andr\'e Markus, Astrid Carolus, Carolin, Wienrich, Andreas Hotho

TL;DR
This paper introduces Higher-Order DeepTrails, a unified method using autoregressive language models to analyze human navigation sequences, capturing complex dependencies and improving hypothesis testing in behavioral analysis.
Contribution
It presents a novel unified approach that models higher-order dependencies in sequence data, adaptable to various existing frameworks like HypTrails and SubTrails.
Findings
Effectively models higher-order dependencies in sequences.
Identifies shortcomings in proposed hypotheses.
Demonstrates applicability on real-world voice assistant data.
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 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…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Topic Modeling · Speech and dialogue systems
