  Motives or goals are recognized in psychology literature as the most
fundamental drive that explains and predicts why people do what they do,
including when they browse the web. Although providing enormous value, these
higher-ordered goals are often unobserved, and little is known about how to
leverage such goals to assist people's browsing activities. This paper proposes
to take a new approach to address this problem, which is fulfilled through a
novel neural framework, Goal-directed Web Browsing (GoWeB). We adopt a
psychologically-sound taxonomy of higher-ordered goals and learn to build their
representations in a structure-preserving manner. Then we incorporate the
resulting representations for enhancing the experiences of common activities
people perform on the web. Experiments on large-scale data from Microsoft Edge
web browser show that GoWeB significantly outperforms competitive baselines for
in-session web page recommendation, re-visitation classification, and
goal-based web page grouping. A follow-up analysis further characterizes how
the variety of human motives can affect the difference observed in human
behavioral patterns.
