  Recent work in commerce search has shown that understanding the semantics in
user queries enables more effective query analysis and retrieval of relevant
products. However, due to lack of sufficient domain knowledge, user queries
often include terms that cannot be mapped directly to any product attribute.
For example, a user looking for {\tt designer handbags} might start with such a
query because she is not familiar with the manufacturers, the price ranges,
and/or the material that gives a handbag designer appeal. Current commerce
search engines treat terms such as {\tt designer} as keywords and attempt to
match them to contents such as product reviews and product descriptions, often
resulting in poor user experience.
  In this study, we propose to address this problem by reformulating queries
involving terms such as {\tt designer}, which we call \emph{modifiers}, to
queries that specify precise product attributes. We learn to rewrite the
modifiers to attribute values by analyzing user behavior and leveraging
structured data sources such as the product catalog that serves the queries. We
first produce a probabilistic mapping between the modifiers and attribute
values based on user behavioral data. These initial associations are then used
to retrieve products from the catalog, over which we infer sets of attribute
values that best describe the semantics of the modifiers. We evaluate the
effectiveness of our approach based on a comprehensive Mechanical Turk study.
We find that users agree with the attribute values selected by our approach in
about 95% of the cases and they prefer the results surfaced for our
reformulated queries to ones for the original queries in 87% of the time.
