24 citations · 60 across the 11 of their papers we have counts for
11 papers
pEBR: A Probabilistic Approach to Embedding Based Retrieval
Han Zhang, Yunjiang Jiang, Mingming Li +3
Embedding-based retrieval aims to learn a shared semantic representation space for both queries and items, enabling efficient and effective item retrieval through approximate neare…
Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification
Chenyu Zhao, Yunjiang Jiang, Yiming Qiu +2
Retrieval augmentation, which enhances downstream models by a knowledge retriever and an external corpus instead of by merely increasing the number of model parameters, has been su…
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
Yiming Qiu, Chenyu Zhao, Han Zhang +7
BERT-style models pre-trained on the general corpus (e.g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: qu…
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…
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…
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…
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…
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…
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…
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…
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…