52 citations · 255 across the 25 of their papers we have counts for
8 papers · 1 filter
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…
Selective Zero-Shot Classification with Augmented Attributes
Jie Song, Chengchao Shen, Jie Lei +4
In this paper, we introduce a selective zero-shot classification problem: how can the classifier avoid making dubious predictions? Existing attribute-based zero-shot classification…
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…
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
Yabo Ni, Dan Ou, Shichen Liu +4
Tasks such as search and recommendation have become increas- ingly important for E-commerce to deal with the information over- load problem. To meet the diverse needs of di erent u…
Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning
Jing-Cheng Shi, Yang Yu, Qing Da +2
Applying reinforcement learning in physical-world tasks is extremely challenging. It is commonly infeasible to sample a large number of trials, as required by current reinforcement…
Dual Swap Disentangling
Zunlei Feng, Xinchao Wang, Chenglong Ke +3
Learning interpretable disentangled representations is a crucial yet challenging task. In this paper, we propose a weakly semi-supervised method, termed as Dual Swap Disentangling…
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection
Yusen Zhan, Qing Da, Fei Xiao +2
In industrial large-scale search systems, such as Taobao.com search for commodities, the quality of the ranking result is getting continually improved by introducing more factors f…
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…