activity
20202022
most citedJoint Learning of Deep Retrieval Model and Product Quantization based Embedding Index

24 citations · 87 across the 20 of their papers we have counts for

collaborators
Showing cs.IRShow all

10 papers · 1 filter

cs.IR2022★ 1 cited

Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes

Yunjiang Jiang, Han Zhang, Yiming Qiu +3

Product quantization (PQ) coupled with a space rotation, is widely used in modern approximate nearest neighbor (ANN) search systems to significantly compress the disk storage for e…

cs.IR2022★ 2 cited

Sequential Search with Off-Policy Reinforcement Learning

Dadong Miao, Yanan Wang, Guoyu Tang +6

Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions be…

cs.IR2021

Intelligent Online Selling Point Extraction for E-Commerce Recommendation

Xiaojie Guo, Shugen Wang, Hanqing Zhao +8

In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. As the services provided by e-commerce platforms become diverse,…

cs.IR2021★ 3 cited

SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search

Xinlin Xia, Shang Wang, Han Zhang +5

Graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully…

cs.IR2021★ 24 cited

Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index

Han Zhang, Hongwei Shen, Yiming Qiu +6

Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems. Traditional approaches…

cs.IR2021

A unified Neural Network Approach to E-CommerceRelevance Learning

Yunjiang Jiang, Yue Shang, Rui Li +5

Result relevance scoring is critical to e-commerce search user experience. Traditional information retrieval methods focus on keyword matching and hand-crafted or counting-based nu…

cs.IR2021★ 1 cited

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

Rui Li, Yunjiang Jiang, Wenyun Yang +7

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 d…

cs.IR2021★ 18 cited

Query Rewriting via Cycle-Consistent Translation for E-Commerce Search

Yiming Qiu, Kang Zhang, Han Zhang +5

Nowadays e-commerce search has become an integral part of many people's shopping routines. One critical challenge in today's e-commerce search is the semantic matching problem wher…

cs.IR2020★ 1 cited

Fine-tune BERT for E-commerce Non-Default Search Ranking

Yunjiang Jiang, Yue Shang, Hongwei Shen +2

The quality of non-default ranking on e-commerce platforms, such as based on ascending item price or descending historical sales volume, often suffers from acute relevance problems…

cs.IR2020★ 4 cited

Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning

Han Zhang, Songlin Wang, Kang Zhang +5

Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retrieve items that are…