  In e-commerce, a user tends to search for the desired product by issuing a
query to the search engine and examining the retrieved results. If the search
engine was successful in correctly understanding the user's query, it will
return results that correspond to the products whose attributes match the terms
in the query that are representative of the query's product intent. However,
the search engine may fail to retrieve results that satisfy the query's product
intent and thus degrading user experience due to different issues in query
processing: (i) when multiple terms are present in a query it may fail to
determine the relevant terms that are representative of the query's product
intent, and (ii) it may suffer from vocabulary gap between the terms in the
query and the product's description, i.e., terms used in the query are
semantically similar but different from the terms in the product description.
Hence, identifying the terms that describe the query's product intent and
predicting additional terms that describe the query's product intent better
than the existing query terms to the search engine is an essential task in
e-commerce search. In this paper, we leverage the historical query
reformulation logs of a major e-commerce retailer to develop distant-supervised
approaches to solve both these problems. Our approaches exploit the fact that
the significance of a term is dependent upon the context (other terms in the
neighborhood) in which it is used in order to learn the importance of the term
towards the query's product intent. We show that identifying and emphasizing
the terms that define the query's product intent leads to a 3% improvement in
ranking. Moreover, for the tasks of identifying the important terms in a query
and for predicting the additional terms that represent product intent,
experiments illustrate that our approaches outperform the non-contextual
baselines.
