  Mapping a search query to a set of relevant categories in the product
taxonomy is a significant challenge in e-commerce search for two reasons: 1)
Training data exhibits severe class imbalance problem due to biased click
behavior, and 2) queries with little customer feedback (e.g., \textit{tail}
queries) are not well-represented in the training set, and cause difficulties
for query understanding. To address these problems, we propose a deep learning
model, DeepCAT, which learns joint word-category representations to enhance the
query understanding process. We believe learning category interactions helps to
improve the performance of category mapping on \textit{minority} classes,
\textit{tail} and \textit{torso} queries. DeepCAT contains a novel
word-category representation model that trains the category representations
based on word-category co-occurrences in the training set. The category
representation is then leveraged to introduce a new loss function to estimate
the category-category co-occurrences for refining joint word-category
embeddings. To demonstrate our model's effectiveness on {\em minority}
categories and {\em tail} queries, we conduct two sets of experiments. The
results show that DeepCAT reaches a 10\% improvement on {\em minority} classes
and a 7.1\% improvement on {\em tail} queries over a state-of-the-art label
embedding model. Our findings suggest a promising direction for improving
e-commerce search by semantic modeling of taxonomy hierarchies.
