research
∙
03/28/2023
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval
Query intent classification, which aims at assisting customers to find d...
share
research
∙
03/20/2023
Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search
Retrieving relevant items that match users' queries from billion-scale c...
research
∙
01/31/2023
ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for E-Commerce Product Search
In this paper, we propose a robust multilingual model to improve the qua...
research
∙
08/12/2022
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
BERT-style models pre-trained on the general corpus (e.g., Wikipedia) an...
research
∙
07/01/2021
SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search
Graph convolution networks (GCN), which recently becomes new state-of-th...
research
∙
05/09/2021
Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index
Embedding index that enables fast approximate nearest neighbor(ANN) sear...
research
∙
03/01/2021
Query Rewriting via Cycle-Consistent Translation for E-Commerce Search
Nowadays e-commerce search has become an integral part of many people's ...
research
∙
06/03/2020
Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
Nowadays e-commerce search has become an integral part of many people's ...
research
∙
06/03/2020
