38 citations · 84 across the 12 of their papers we have counts for
12 papers
Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences
Qianying Lin, Wen-Ji Zhou, Yanshi Wang +3
Sequential recommendation predicts users' next behaviors with their historical interactions. Recommending with longer sequences improves recommendation accuracy and increases the d…
A General Traffic Shaping Protocol in E-Commerce
Chenlin Shen, Guangda Huzhang, Yuhang Zhou +2
To approach different business objectives, online traffic shaping algorithms aim at improving exposures of a target set of items, such as boosting the growth of new commodities. Ge…
Imitate TheWorld: A Search Engine Simulation Platform
Yongqing Gao, Guangda Huzhang, Weijie Shen +4
Recent E-commerce applications benefit from the growth of deep learning techniques. However, we notice that many works attempt to maximize business objectives by closely matching o…
Learning-To-Ensemble by Contextual Rank Aggregation in E-Commerce
Xuesi Wang, Guangda Huzhang, Qianying Lin +1
Ensemble models in E-commerce combine predictions from multiple sub-models for ranking and revenue improvement. Industrial ensemble models are typically deep neural networks, follo…
Diversity Regularized Interests Modeling for Recommender Systems
Junmei Hao, Jingcheng Shi, Qing Da +4
With the rapid development of E-commerce and the increase in the quantity of items, users are presented with more items hence their interests broaden. It is increasingly difficult…
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:…
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…
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…
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…
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…