Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System ICML 2019 摘要： 本文是推薦系統和強化學習結合起來的應用： 主要是2點： 1 我們開發了一個生成式的對抗學習(GAN)公式來模擬用戶的行爲動態，並恢復她的獎勵功能。通過聯合最小二乘優化算法，同時對這兩個分量進
相關文章
- 1. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 2. AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets
- 3. 論文筆記 Benchmarking Model-Based Reinforcement Learning
- 4. Model-Based Reinforcement Learning: Theory and Practice 譯文
- 5. MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走讀
- 6. [Reinforcement Learning] Model-Free Prediction
- 7. Reinforcement Learning（二）：Value-Based
- 8. [Reinforcement Learning] Model-Free Control
- 9. Reinforcement Learning（三）：Policy-Based
- 10. Reinforcement Learning強化學習系列之一：model-based learning
- 更多相關文章...
- • XSLT system-property() 函數 - XSLT 教程
- • Swift for 循環 - Swift 教程
- • Java Agent入門實戰（三）-JVM Attach原理與使用
- • Flink 數據傳輸及反壓詳解
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 2. AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets
- 3. 論文筆記 Benchmarking Model-Based Reinforcement Learning
- 4. Model-Based Reinforcement Learning: Theory and Practice 譯文
- 5. MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走讀
- 6. [Reinforcement Learning] Model-Free Prediction
- 7. Reinforcement Learning（二）：Value-Based
- 8. [Reinforcement Learning] Model-Free Control
- 9. Reinforcement Learning（三）：Policy-Based
- 10. Reinforcement Learning強化學習系列之一：model-based learning