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

24 citations · 38 across the 10 of their papers we have counts for

collaborators

14 papers

cs.AI20222 cited

Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce

Xueying Zhang, Kai Shen, Chi Zhang +5

In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese…

cs.LG2022

Reducing Flipping Errors in Deep Neural Networks

Xiang Deng, Yun Xiao, Bo Long +1

Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing. A DNN is typically tra…

cs.IR20221 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.IR20222 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.IR20213 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.IR202124 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…