  An accurate understanding of a user's query intent can help improve the
performance of downstream tasks such as query scoping and ranking. In the
e-commerce domain, recent work in query understanding focuses on the query to
product-category mapping. But, a small yet significant percentage of queries
(in our website 1.5% or 33M queries in 2019) have non-commercial intent
associated with them. These intents are usually associated with non-commercial
information seeking needs such as discounts, store hours, installation guides,
etc. In this paper, we introduce Joint Query Intent Understanding (JointMap), a
deep learning model to simultaneously learn two different high-level user
intent tasks: 1) identifying a query's commercial vs. non-commercial intent,
and 2) associating a set of relevant product categories in taxonomy to a
product query. JointMap model works by leveraging the transfer bias that exists
between these two related tasks through a joint-learning process. As curating a
labeled data set for these tasks can be expensive and time-consuming, we
propose a distant supervision approach in conjunction with an active learning
model to generate high-quality training data sets. To demonstrate the
effectiveness of JointMap, we use search queries collected from a large
commercial website. Our results show that JointMap significantly improves both
"commercial vs. non-commercial" intent prediction and product category mapping
by 2.3% and 10% on average over state-of-the-art deep learning methods. Our
findings suggest a promising direction to model the intent hierarchies in an
e-commerce search engine.
