142 citations
- Microsoft (United States)US9 papers
- Jingdong (China)CN6 papers
- Stanford UniversityUS3 papers
- Cornell UniversityUS1 paper
- Duke UniversityUS1 paper
- Environmental and Occupational Health Sciences InstituteUS1 paper
- Georgia Institute of TechnologyUS1 paper
- Google (United States)US1 paper
- Hewlett-Packard (United States)US1 paper
- IBM Research - AlmadenUS1 paper
- JDSU (United States)US1 paper
- Natural Sciences and Engineering Research Council of CanadaCA1 paper
19 papers
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…
Automatic Controllable Product Copywriting for E-Commerce
Xiaojie Guo, Qingkai Zeng, Meng Jiang +3
Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copywriting aims to attract users' interest and improve us…
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…
DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization
Xueying Zhang, Yunjiang Jiang, Yue Shang +5
We propose a novel domain-specific generative pre-training (DS-GPT) method for text generation and apply it to the product titleand review summarization problems on E-commerce mobi…
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
Yitong Pang, Lingfei Wu, Qi Shen +6
Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition pattern…
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…