HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web

TL;DR

HypTrails introduces a Bayesian framework using Markov chains and Dirichlet priors to compare hypotheses about human web trails, aiding understanding of user behavior across various online activities.

Contribution

The paper presents HypTrails, a novel Bayesian approach that incorporates hypotheses as priors for comparing models of human trails on the Web, with a new method for eliciting priors from hypotheses.

Findings

Effective in synthetic trail experiments

Applicable to real-world web navigation data

Enhances understanding of user behavior patterns

Abstract

When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.