  Online shopping platforms, such as Amazon, offer services to billions of
people worldwide. Unlike web search or other search engines, product search
engines have their unique characteristics, primarily featuring short queries
which are mostly a combination of product attributes and structured product
search space. The uniqueness of product search underscores the crucial
importance of the query understanding component. However, there are limited
studies focusing on exploring this impact within real-world product search
engines. In this work, we aim to bridge this gap by conducting a comprehensive
study and sharing our year-long journey investigating how the query
understanding service impacts Amazon Product Search. Firstly, we explore how
query understanding-based ranking features influence the ranking process. Next,
we delve into how the query understanding system contributes to understanding
the performance of a ranking model. Building on the insights gained from our
study on the evaluation of the query understanding-based ranking model, we
propose a query understanding-based multi-task learning framework for ranking.
We present our studies and investigations using the real-world system on Amazon
Search.
