  Query categorization is an essential part of query intent understanding in
e-commerce search. A common query categorization task is to select the relevant
fine-grained product categories in a product taxonomy. For frequent queries,
rich customer behavior (e.g., click-through data) can be used to infer the
relevant product categories. However, for more rare queries, which cover a
large volume of search traffic, relying solely on customer behavior may not
suffice due to the lack of this signal. To improve categorization of rare
queries, we adapt the Pseudo-Relevance Feedback (PRF) approach to utilize the
latent knowledge embedded in semantically or lexically similar product
documents to enrich the representation of the more rare queries. To this end,
we propose a novel deep neural model named \textbf{A}ttentive \textbf{P}seudo
\textbf{R}elevance \textbf{F}eedback \textbf{Net}work (APRF-Net) to enhance the
representation of rare queries for query categorization. To demonstrate the
effectiveness of our approach, we collect search queries from a large
commercial search engine, and compare APRF-Net to state-of-the-art deep
learning models for text classification. Our results show that the APRF-Net
significantly improves query categorization by 5.9\% on $F1@1$ score over the
baselines, which increases to 8.2\% improvement for the rare (tail) queries.
The findings of this paper can be leveraged for further improvements in search
query representation and understanding.
