  Query intent classification, which aims at assisting customers to find
desired products, has become an essential component of the e-commerce search.
Existing query intent classification models either design more exquisite models
to enhance the representation learning of queries or explore label-graph and
multi-task to facilitate models to learn external information. However, these
models cannot capture multi-granularity matching features from queries and
categories, which makes them hard to mitigate the gap in the expression between
informal queries and categories.
  This paper proposes a Multi-granularity Matching Attention Network (MMAN),
which contains three modules: a self-matching module, a char-level matching
module, and a semantic-level matching module to comprehensively extract
features from the query and a query-category interaction matrix. In this way,
the model can eliminate the difference in expression between queries and
categories for query intent classification. We conduct extensive offline and
online A/B experiments, and the results show that the MMAN significantly
outperforms the strong baselines, which shows the superiority and effectiveness
of MMAN. MMAN has been deployed in production and brings great commercial value
for our company.
