  We introduce deep learning models to the two most important stages in product
search at JD.com, one of the largest e-commerce platforms in the world.
Specifically, we outline the design of a deep learning system that retrieves
semantically relevant items to a query within milliseconds, and a pairwise deep
re-ranking system, which learns subtle user preferences. Compared to
traditional search systems, the proposed approaches are better at semantic
retrieval and personalized ranking, achieving significant improvements.
