  Product search is the most common way for people to satisfy their shopping
needs on e-commerce websites. Products are typically annotated with one of
several broad categorical tags, such as "Clothing" or "Electronics", as well as
finer-grained categories like "Refrigerator" or "TV", both under "Electronics".
These tags are used to construct a hierarchy of query categories. Distributions
of features such as price and brand popularity vary wildly across query
categories. In addition, feature importance for the purpose of CTR/CVR
predictions differs from one category to another. In this work, we leverage the
Mixture of Expert (MoE) framework to learn a ranking model that specializes for
each query category. In particular, our gate network relies solely on the
category ids extracted from the user query. While classical MoE's pick expert
towers spontaneously for each input example, we explore two techniques to
establish more explicit and transparent connections between the experts and
query categories. To help differentiate experts on their domain specialties, we
introduce a form of adversarial regularization among the expert outputs,
forcing them to disagree with one another. As a result, they tend to approach
each prediction problem from different angles, rather than copying one another.
This is validated by a much stronger clustering effect of the gate output
vectors under different categories. In addition, soft gating constraints based
on the categorical hierarchy are imposed to help similar products choose
similar gate values. and make them more likely to share similar experts. This
allows aggregation of training data among smaller sibling categories to
overcome data scarcity.
