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Understanding human activities and movements on the Web is not only importantfor computational social scientists but can also offer valuable guidance forthe design of online systems for recommendations, caching, advertising, andpersonalization. In this work, we demonstrate that people tend to followroutines on the Web, and these repetitive patterns of web visits increase theirbrowsing behavior's achievable predictability. We present aninformation-theoretic framework for measuring the uncertainty and theoreticallimits of predictability of human mobility on the Web. We systematically assessthe impact of different design decisions on the measurement. We apply theframework to a web tracking dataset of German internet users. Our empiricalresults highlight that individual's routines on the Web make their browsingbehavior predictable to 85% on average, though the value varies acrossindividuals. We observe that these differences in the users' predictabilitiescan be explained to some extent by their demographic and behavioral attributes.

Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data