24 citations · 42 across the 10 of their papers we have counts for
5 papers · 1 filter
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…