From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search
This work addresses the problem of enhancing search relevance and personalization for users on a major e-commerce platform, but it appears incremental as it applies existing deep learning techniques to a specific domain.
The paper tackled improving product search at JD.com by introducing deep learning models for semantic retrieval and pairwise re-ranking, achieving significant improvements over traditional systems.
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.