  The aggregate behaviors of users can collectively encode deep semantic
information about the objects with which they interact. In this paper, we
demonstrate novel ways in which the synthesis of these data can illuminate the
terrain of users' environment and support them in their decision making and
wayfinding. A novel application of Recurrent Neural Networks and skip-gram
models, approaches popularized by their application to modeling language, are
brought to bear on student university enrollment sequences to create vector
representations of courses and map out traversals across them. We present
demonstrations of how scrutability from these neural networks can be gained and
how the combination of these techniques can be seen as an evolution of content
tagging and a means for a recommender to balance user preferences inferred from
data with those explicitly specified. From validation of the models to the
development of a UI, we discuss additional requisite functionality informed by
the results of a usability study leading to the ultimate deployment of the
system at a university.
