  Clickstreams on individual websites have been studied for decades to gain
insights into user interests and to improve website experiences. This paper
proposes and examines a novel sequence modeling approach for web clickstreams,
that also considers multi-tab branching and backtracking actions across
websites to capture the full action sequence of a user while browsing. All of
this is done using machine learning on the client side to obtain a more
comprehensive view and at the same time preserve privacy. We evaluate our
formalism with a model trained on data collected in a user study with three
different browsing tasks based on different human information seeking
strategies from psychological literature. Our results show that the model can
successfully distinguish between browsing behaviors and correctly predict
future actions. A subsequent qualitative analysis identified five common web
browsing patterns from our collected behavior data, which help to interpret the
model. More generally, this illustrates the power of overparameterization in ML
and offers a new way of modeling, reasoning with, and prediction of observable
sequential human interaction behaviors.
