52 citations · 255 across the 25 of their papers we have counts for
8 papers · 1 filter
Recurrent Temporal Revision Graph Networks
Yizhou Chen, Anxiang Zeng, Guangda Huzhang +6
Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for tempor…
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
Yanshi Wang, Jie Zhang, Qing Da +1
Estimating post-click conversion rate (CVR) accurately is crucial in E-commerce. However, CVR prediction usually suffers from three major challenges in practice: i) data sparsity:…
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
Jianxiong Wei, Anxiang Zeng, Yueqiu Wu +3
The slate re-ranking problem considers the mutual influences between items to improve user satisfaction in e-commerce, compared with the point-wise ranking. Previous works either d…
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao +8
Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most existing LTR approaches follow a supervised learning paradigm from offline labeled data collecte…
Matrix embedding method in match for session-based recommendation
Qizhi Zhang, Yi Lin, Kangle Wu +2
Session based model is widely used in recommend system. It use the user click sequence as input of a Recurrent Neural Network (RNN), and get the output of the RNN network as the ve…
Policy Optimization with Model-based Explorations
Feiyang Pan, Qingpeng Cai, An-Xiang Zeng +5
Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games…
Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design
Hua-Lin He, Chun-Xiang Pan, Qing Da +1
In a large E-commerce platform, all the participants compete for impressions under the allocation mechanism of the platform. Existing methods mainly focus on the short-term return…
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
Yujing Hu, Qing Da, Anxiang Zeng +2
In e-commerce platforms such as Amazon and TaoBao, ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widel…