2 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
Jiongnan Liu, Zhicheng Dou, Guoyu Tang +1
Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize th…
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…
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…
Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
Zhuojian Xiao, Yunjiang jiang, Guoyu Tang +4
Product search is the most common way for people to satisfy their shopping needs on e-commerce websites. Products are typically annotated with one of several broad categorical tags…