25 citations · 106 across the 23 of their papers we have counts for
10 papers · 1 filter
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…
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce
Juan Gong, Zhenlin Chen, Chaoyi Ma +7
Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the use…
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…
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…
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…
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search
Ziyang Liu, Zhaomeng Cheng, Yunjiang Jiang +5
Result relevance prediction is an essential task of e-commerce search engines to boost the utility of search engines and ensure smooth user experience. The last few years eyewitnes…
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…