Search Results for author: Qing Da

Found 11 papers, 2 papers with code

A General Traffic Shaping Protocol in E-Commerce

no code implementations30 Dec 2021 Chenlin Shen, Guangda Huzhang, YuHang Zhou, Chen Liang, Qing Da

Our algorithm can straightforwardly optimize the linear programming in the prime space, and its solution can be simply applied by a stochastic strategy to fulfill the optimized objective and the constraints in expectation.

Iterative Memory Network for Long Sequential User Behavior Modeling in Recommender Systems

no code implementations29 Sep 2021 Qianying Lin, Wen-Ji Zhou, Yanshi Wang, Qing Da, Qing-Guo Chen, Bing Wang

Extensive empirical studies show that our method outperforms various state-of-the-art sequential modeling methods on both public and industrial datasets for long sequential user behavior modeling.

Recommendation Systems

Learning-To-Ensemble by Contextual Rank Aggregation in E-Commerce

no code implementations19 Jul 2021 Xuesi Wang, Guangda Huzhang, Qianying Lin, Qing Da

Combined with the idea of Bayesian Optimization and gradient descent, we solve the online contextual Black-Box Optimization task that finds the optimal weights for sub-models given a chosen RA model.

Diversity Regularized Interests Modeling for Recommender Systems

no code implementations23 Mar 2021 Junmei Hao, JingCheng Shi, Qing Da, AnXiang Zeng, Yujie Dun, Xueming Qian, Qianying Lin

Each interest of the user should have a certain degree of distinction, thus we introduce three strategies as the diversity regularized separator to separate multiple user interest vectors.

Recommendation Systems

Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction

no code implementations24 Nov 2020 Yanshi Wang, Jie Zhang, Qing Da, AnXiang Zeng

In this paper, we propose a novel neural network framework ESDF to tackle the above three challenges simultaneously.

Selection bias Survival Analysis

AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online

no code implementations25 Mar 2020 Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Yawen Liu, Weijie Shen, Wen-Ji Zhou, Qing Da, An-Xiang Zeng, Han Yu, Yang Yu, Zhi-Hua Zhou

The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator that ensures the generalization of the evaluator.

Learning-To-Rank

Policy Optimization with Model-based Explorations

no code implementations18 Nov 2018 Feiyang Pan, Qingpeng Cai, An-Xiang Zeng, Chun-Xiang Pan, Qing Da, Hua-Lin He, Qing He, Pingzhong Tang

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.

Atari Games Decision Making +2

Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design

no code implementations2 Jul 2018 Hua-Lin He, Chun-Xiang Pan, Qing Da, An-Xiang Zeng

In a large E-commerce platform, all the participants compete for impressions under the allocation mechanism of the platform.

Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application

1 code implementation2 Mar 2018 Yujing Hu, Qing Da, An-Xiang Zeng, Yang Yu, Yinghui Xu

For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session.

Decision Making Learning-To-Rank +1

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