71 citations
- Microsoft (United States)US5 papers
- Jingdong (China)CN3 papers
- Cornell UniversityUS1 paper
- Hewlett-Packard (United States)US1 paper
- JDSU (United States)US1 paper
- South China University of TechnologyCN1 paper
- Stanford UniversityUS1 paper
- University of California, BerkeleyUS1 paper
- University of Hong KongHK1 paper
5 papers · 1 filter
Multi-Behavior Graph Neural Networks for Recommender System
Lianghao Xia, Chao Huang, Yong Xu +2
Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recen…
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…
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…
Structured Query Reformulations in Commerce Search
Sreenivas Gollapudi, Samuel Ieong, Anitha Kannan
Recent work in commerce search has shown that understanding the semantics in user queries enables more effective query analysis and retrieval of relevant products. However, due to…