Comparing Hypotheses about Human Trails on the Web

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 talk, I 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. The approach utilizes Bayesian inference and the core idea is to incorporate hypotheses as priors into the inference process and utilize the sensitivity of Bayes factors on the prior for gaining insights into the relative plausibility of hypotheses. I will also present results from empirical experiments studying several different kinds of human trails such as (i) human navigational trails , (ii) online editing trails or (iii) human mobility trails. Bio: Dr. Philipp Singer is a post doctoral researcher at the Computational Social Science Department of the Leibniz Institute for the Social Sciences (GESIS) in Cologne (Germany). Philipp is interested in data science, statistics with a focus on Bayesian statistics, machine learning and web science. In the past few years, Philipp has been mainly concerned with modeling aspects of human trails on the Web. Human trails can emerge by any kind of human interaction with the Web such as the navigation of websites. In detail, he has been dedicated to provide tools that facilitate future research concerned with the study of regularities, patterns and strategies in human trails on the Web. Philipp has published his work in top-tier conferences and journals such as WWW, ISWC, CIKM or IJHCS.