# From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search

arXiv: https://arxiv.org/abs/2103.12982 · HTML: https://mlsanity.com/paper/2103.12982/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)

## Built on by (most cited first, 2 of 2)

- [(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis](https://mlsanity.com/paper/2304.11473/) (2023)
- [Semantic Retrieval for Product Search in E-Commerce](https://mlsanity.com/paper/2606.01504/) (2026)

## Abstract (arXiv)

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.