Topics | Papers of RL4Recsys

[aimiyu]

综述

2019,arxiv,. Reinforcement Learning Applications 2019,CSUR,. Deep learning based recommender system: A survey and new perspectives

RL基础

业界papers

• 2019, IJCAI, Google. Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
• 2019, WSDM, Google. Top-K Off-Policy Correction for a REINFORCE Recommender System
• 2019, Google. SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
• 2019,ICML, Ant Financial. Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
• 2019, AAAI, Alibaba. Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning
• 2019, WWW. Alibaba. Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems
• 2018, KDD, Alibaba. Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
• Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
• 2018, WWW, Microsoft. DRN: A Deep Reinforcement Learning Framework for News Recommendation
• 2020, WSDM, JD. Model-Based Reinforcement Learning for Whole-Chain Recommendations
• 2019, KDD, JD. Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
• 2019, KDD, JD. Off-policy Learning for Multiple Loggers
• 2018, KDD, JD. Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
• 2018, Recsys, JD. Deep reinforcement learning for page-wise recommendations
• 2018, arxiv, JD. Deep Reinforcement Learning for List-wise Recommendations
• 2019, AAAI, Huawei. Large-scale Interactive Recommendation with Tree-structured Policy Gradient
• 2015, arxiv, Google. Deep Reinforcement Learning in Large Discrete Action Spaces

[aimiyu]

Good refs：https://github.com/cszhangzhen/DRL4Recsys