  In an attempt to balance precision and recall in the search page, leading
digital shops have been effectively nudging users into select category facets
as early as in the type-ahead suggestions. In this work, we present
SessionPath, a novel neural network model that improves facet suggestions on
two counts: first, the model is able to leverage session embeddings to provide
scalable personalization; second, SessionPath predicts facets by explicitly
producing a probability distribution at each node in the taxonomy path. We
benchmark SessionPath on two partnering shops against count-based and neural
models, and show how business requirements and model behavior can be combined
in a principled way.
